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Understanding GANs: fundamentals, variants, training challenges, applications, and open problems

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Abstract

Generative adversarial networks (GANs), a novel framework for training generative models in an adversarial setup, have attracted significant attention in recent years. The two opposing neural networks of the GANs framework, i.e., a generator and a discriminator, are trained simultaneously in a zero-sum game, where the generator generates images to fool the discriminator that is trained to discriminate between real and synthetic images. In this paper, we provide a comprehensive review about the recent developments in GANs. Firstly, we introduce various deep generative models, basic theory and training mechanism of GANs, and the latent space. We further discuss several representative variants of GANs. Although GANs have been successfully utilized in various applications, they are known to be highly unstable to train. Generally, there is a lack of understanding as to how GANs converge. We briefly discuss the sources of instability and convergence issues in GANs from the perspectives of statistics, game theory and control theory, and describe several techniques for their stable training. Evaluating GANs has been a challenging task, as there is no consensus yet reached on which measure is more suitable for model comparison. Therefore, we provide a brief discussion on quantitative and qualitative evaluation measures for GANs. Then, we conduct several experiments to compare representative GANs variants based on these evaluation metrics. Furthermore, the application areas of GANs are briefly discussed. Finally, we outline several important open issues and future research trends in GANs.

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Data availability

The datasets analyzed during this study were derived from the following publicly available domain resources: ImageNet: https://www.image-net.org; CIFAR10: http://www.cs.toronto.edu/\(\sim \)kriz/cifar.html; AFHQ: https://github.com/clovaai/stargan-v2; FFHQ: https://github.com/NVlabs/ffhq-dataset; CelebA: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html.

Code Availability

The codes used to generate various figures and tables in current study are available from the corresponding author upon reasonable request.

References

  1. Li Q, Qu H, Liu Z et al (2021) Af-dcgan: Amplitude feature deep convolutional gan for fingerprint construction in indoor localization systems. IEEE Trans Emerg Top Comput Intell 5(3):468–480. https://doi.org/10.1109/TETCI.2019.2948058

    Article  Google Scholar 

  2. Zheng K, Yan WQ, Nand P (2018) Video dynamics detection using deep neural networks. IEEE Trans Emerg Top Comput Intell 2(3):224–234. https://doi.org/10.1109/TETCI.2017.2778716

    Article  Google Scholar 

  3. Kuppili V, Biswas M, Edla DR et al (2020) A mechanics-based similarity measure for text classification in machine learning paradigm. IEEE Trans Emerg Top Comput Intell 4(2):180–200. https://doi.org/10.1109/TETCI.2018.2863728

    Article  Google Scholar 

  4. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  Google Scholar 

  5. Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Proceedings of the 12th international conference on artificial intelligence and statistics, Clearwater Beach, Florida, USA, pp 448–455

  6. Rumelhart DE, McClelland JL (1987) Information processing in dynamical systems: foundations of harmony theory. Parallel distributed processing: explorations in the microstructure of cognition: foundations. MIT Press, Cambridge, MA, pp 194–281

    Google Scholar 

  7. Mackay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, New York, NY, United States

    Google Scholar 

  8. Hastings WK (1970) Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1):97–109. https://doi.org/10.2307/2334940

    Article  MathSciNet  Google Scholar 

  9. Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800. https://doi.org/10.1162/089976602760128018

    Article  Google Scholar 

  10. Hyvärinen A (2005) Estimation of non-normalized statistical models by score matching. J Mach Learn Res 6(24):695–709

    MathSciNet  Google Scholar 

  11. Hyvärinen A, Hurri J, Hoyer PO (2009) Estimation of non-normalized statistical models. In: Natural image statistics: a probabilistic approach to early computational vision. Springer London, London, p 419–426, https://doi.org/10.1007/978-1-84882-491-1_21

  12. Gutmann MU, Hyvärinen A (2010) Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the 13th international conference on artificial intelligence and statistics, Chia Laguna Resort, Sardinia, Italy, pp 297–304

  13. Gutmann MU, Hyvarinen A (2012) Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J Mach Learn Res 13(11):307–361

    MathSciNet  Google Scholar 

  14. Ceylan C, Gutmann MU (2018) Conditional noise-contrastive estimation of unnormalised models. In: Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, pp 726–734

  15. Rhodes B, Gutmann MU (2019) Variational noise-contrastive estimation. In: Proceedings of the 22nd international conference on artificial intelligence and statistics, Naha, Okinawa, Japan, pp 2741–2750

  16. Zhang W, Stratos K (2021) Understanding hard negatives in noise contrastive estimation. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, [Virtual], pp 1090–1101

  17. Tieleman T (2008) Training restricted boltzmann machines using approximations to the likelihood gradient. In: Proceedings of the 25th international conference on machine learning, Helsinki, Finland, pp 1064–1071, https://doi.org/10.1145/1390156.1390290

  18. Tieleman T, Hinton GE (2009) Using fast weights to improve persistent contrastive divergence. In: Proceedings of the 26th international conference on machine learning, Quebec, Canada, pp 1033–1040, https://doi.org/10.1145/1553374.1553506

  19. Neal RM (1996) Sampling from multimodal distributions using tempered transitions. Stat Comput 6:353–366. https://doi.org/10.1007/BF00143556

    Article  Google Scholar 

  20. Desjardins G, Courville A, Bengio Y et al (2009) Parallel tempering for training of restricted boltzmann machines. In: Proceedings of the 13th international conference on artificial intelligence and statistics, Chia Laguna Resort, Sardinia, Italy, pp 145–152

  21. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge, MA

    Google Scholar 

  22. Gm H, Gourisaria MK, Pandey M et al (2020) A comprehensive survey and analysis of generative models in machine learning. Comput Sci Rev 38:100285. https://doi.org/10.1016/j.cosrev.2020.100285

    Article  MathSciNet  Google Scholar 

  23. Goodfellow IJ, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems, Montréal, Canada, pp 2672–2680

  24. Chen X, Duan Y, Houthooft R et al (2016) Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 2180–2188

  25. Jeon I, Lee W, Pyeon M et al (2021) Ib-gan: disentangled representation learning with information bottleneck generative adversarial networks. Proceedings of the AAAI conference on artificial intelligence 35(9):7926–7934. https://doi.org/10.1609/aaai.v35i9.16967

  26. Odena A, Buckman J, Olsson C et al (2018) Is generator conditioning causally related to gan performance? In: Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, pp 3849–3858

  27. Mescheder L, Geiger A, Nowozin S (2018) Which training methods for gans do actually converge? In: Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, pp 3481–3490

  28. Lin Z, Sekar V, Fanti G (2021) Why spectral normalization stabilizes gans: Analysis and improvements. In: Proceedings of the 35th international conference on neural information processing systems, [Virtual], pp 9625–9638

  29. Farnia F, Ozdaglar A (2020a) Gans may have no nash equilibria. https://doi.org/10.48550/arXiv.2002.09124. arXiv:2002.09124

  30. Farnia F, Ozdaglar A (2020b) Do gans always have nash equilibria? In: Proceedings of the 37th international conference on machine learning, [Virtual], pp 3029–3039

  31. Salimans T, Goodfellow I, Zaremba W et al (2016) Improved techniques for training gans. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 2226–2234

  32. Sinha S, Zhao Z, Goyal A et al (2020) Top-k training of gans: improving gan performance by throwing away bad samples. In: Proceedings of the 34th international conference on neural information processing systems, [Virtual], pp 14638–14649

  33. Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–17, available at https://openreview.net/forum?id=Hk4_qw5xe

  34. Mescheder L, Nowozin S, Geiger A (2017) The numerics of gans. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 1823–1833

  35. Zhao J, Mathieu M, LeCun Y (2017) Energy-based generative adversarial network. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–17, available at https://openreview.net/forum?id=ryh9pmcee

  36. Borji A (2019) Pros and cons of gan evaluation measures. Comput Vis Image Underst 179:41–65. https://doi.org/10.1016/j.cviu.2018.10.009

    Article  Google Scholar 

  37. Borji A (2022) Pros and cons of gan evaluation measures: new developments. Comput Vis Image Underst 215:103329. https://doi.org/10.1016/j.cviu.2021.103329

    Article  Google Scholar 

  38. Wang Z, She Q, Ward TE (2021) Generative adversarial networks in computer vision: a survey and taxonomy. ACM Comput Surv 54(2):37. https://doi.org/10.1145/3439723

    Article  Google Scholar 

  39. Aggarwal A, Mittal M, Battineni G (2021) Generative adversarial network: an overview of theory and applications. Int J Inf Manag Data Insights 1(1):100004. https://doi.org/10.1016/j.jjimei.2020.100004

    Article  Google Scholar 

  40. Toshpulatov M, Lee W, Lee S (2021) Generative adversarial networks and their application to 3d face generation: a survey. Image Vis Comput 108:104119. https://doi.org/10.1016/j.imavis.2021.104119

    Article  Google Scholar 

  41. Kammoun A, Slama R, Tabia H et al (2022) Generative adversarial networks for face generation: A survey. ACM Comput Surv 55(5):94. https://doi.org/10.1145/3527850

    Article  Google Scholar 

  42. Frolov S, Hinz T, Raue F et al (2021) Adversarial text-to-image synthesis: a review. Neural Netw 144:187–209. https://doi.org/10.1016/j.neunet.2021.07.019

    Article  Google Scholar 

  43. Zhou R, Jiang C, Xu Q (2021) A survey on generative adversarial network-based text-to-image synthesis. Neurocomputing 451:316–336. https://doi.org/10.1016/j.neucom.2021.04.069

    Article  Google Scholar 

  44. Navidan H, Moshiri PF, Nabati M et al (2021) Generative adversarial networks (gans) in networking: A comprehensive survey & evaluation. Comput Netw 194:108149. https://doi.org/10.1016/j.comnet.2021.108149

    Article  Google Scholar 

  45. Pan Z, Yu W, Yi X et al (2019) Recent progress on generative adversarial networks (gans): a survey. IEEE Access 7:36322–36333. https://doi.org/10.1109/ACCESS.2019.2905015

    Article  Google Scholar 

  46. Wang K, Gou C, Duan Y et al (2017) Generative adversarial networks: introduction and outlook. IEEE-CAA J Automatica Sin 4(4):588–598. https://doi.org/10.1109/JAS.2017.7510583

    Article  MathSciNet  Google Scholar 

  47. Li Y, Wang Q, Zhang J et al (2021) The theoretical research of generative adversarial networks: An overview. Neurocomputing 435:26–41. https://doi.org/10.1016/j.neucom.2020.12.114

    Article  Google Scholar 

  48. Pan Z, Yu W, Wang B et al (2020) Loss functions of generative adversarial networks (gans): opportunities and challenges. IEEE Trans Emerg Top Comput Intell 4(4):500–522. https://doi.org/10.1109/TETCI.2020.2991774

    Article  Google Scholar 

  49. Hong Y, Hwang U, Yoo J et al (2020) How generative adversarial networks and their variants work: an overview. ACM Comput Surv 52(1):10. https://doi.org/10.1145/3301282

    Article  Google Scholar 

  50. Gui J, Sun Z, Wen Y et al (2023) A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans Knowl Data Eng 35(4):3313–3332.https://doi.org/10.1109/TKDE.2021.3130191

  51. Liu MY, Huang X, Yu J et al (2021) Generative adversarial networks for image and video synthesis: algorithms and applications. Proceedings of the IEEE 109(5):839–862. https://doi.org/10.1109/JPROC.2021.3049196

    Article  Google Scholar 

  52. Jabbar A, Li X, Omar B (2021) A survey on generative adversarial networks: Variants, applications, and training. ACM Comput Surv 54(8):157. https://doi.org/10.1145/3463475

    Article  Google Scholar 

  53. Moghaddam MM, Boroomand B, Jalali M et al (2023) Games of gans: game-theoretical models for generative adversarial networks. Artif Intell Rev 56:9771–9807. https://doi.org/10.1007/s10462-023-10395-6

    Article  Google Scholar 

  54. Iglesias G, Talavera E, Díaz-Álvarez A (2023) A survey on gans for computer vision: recent research, analysis and taxonomy. Comput Sci Rev 48:100553. https://doi.org/10.1016/j.cosrev.2023.100553

  55. Dash A, Ye J, Wang G (2023) A review of generative adversarial networks (gans) and its applications in a wide variety of disciplines: from medical to remote sensing. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3346273

    Article  Google Scholar 

  56. Bond-Taylor S, Leach A, Long Y et al (2022) Deep generative modelling: a comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Trans Pattern Anal Mach Intell 44(11):7327–7347. https://doi.org/10.1109/TPAMI.2021.3116668

    Article  Google Scholar 

  57. Wu Q, Gao R, Zha H (2021) Bridging explicit and implicit deep generative models via neural stein estimators. In: Proceedings of the 35th international conference on neural information processing systems, [Virtual], pp 11274–11286

  58. Xie J, Lu Y, Zhu SC et al (2016) A theory of generative convnet. In: Proceedings of the 33rd International Conference on Machine Learning, New York City, NY, USA, pp 2635–2644

  59. Liu Q, Xu J, Jiang R et al (2021) Density estimation using deep generative neural networks. Proceedings of the national academy of sciences 118(15):e2101344118. https://doi.org/10.1073/pnas.2101344118

    Article  MathSciNet  Google Scholar 

  60. van den Oord A, Kalchbrenner N, Vinyals O et al (2016a) Conditional image generation with pixelcnn decoders. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 4790–4798

  61. van den Oord A, Kalchbrenner N, Kavukcuoglu K (2016b) Pixel recurrent neural networks. In: Proceedings of the 33rd international conference on machine learning, New York, NY, USA, pp 1747–1756

  62. Uria B, Côté MA, Gregor K et al (2016) Neural autoregressive distribution estimation. J Mach Learn Res 17(205):1–37

    MathSciNet  Google Scholar 

  63. Germain M, Gregor K, Murray I et al (2015) Made: masked autoencoder for distribution estimation. In: Proceedings of the 32nd international conference on machine learning, Lille, France, pp 881–889

  64. Nguyen A, Dosovitskiy A, Yosinski J et al (2016) Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 3387–3395

  65. van Ravenzwaaij D, Cassey P, Brown SD (2018) A simple introduction to markov chain monte–carlo sampling. Psychon Bull Rev 25:143–154. https://doi.org/10.3758/s13423-016-1015-8

    Article  Google Scholar 

  66. Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: a review for statisticians. J Am Stat Assoc 112(518):859–877. https://doi.org/10.1080/01621459.2017.1285773

    Article  MathSciNet  Google Scholar 

  67. Fatir Ansari A, Scarlett J, Soh H (2020) A characteristic function approach to deep implicit generative modeling. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 7476–7484. https://doi.org/10.1109/CVPR42600.2020.00750

  68. Li K, Malik J (2018) Implicit maximum likelihood estimation. https://doi.org/10.48550/arXiv.1809.09087. arXiv:1809.09087v2

  69. Bengio Y, Thibodeau-Laufer E, Alain G et al (2014) Deep generative stochastic networks trainable by backprop. In: Proceedings of the 31st international conference on machine learning, Beijing, China, pp 226–234

  70. Bengio Y, Yao L, Alain G et al (2013) Generalized denoising auto-encoders as generative models. In: Proceedings of the 26th international conference on neural information processing systems, Lake Tahoe, Nevada, USA, pp 899–907

  71. Ngwenduna KS, Rendani M (2021) Alleviating class imbalance in actuarial applications using generative adversarial networks. Risks 9(3):49. https://doi.org/10.3390/risks9030049

    Article  Google Scholar 

  72. Li Y, Swersky K, Zemel R (2015) Generative moment matching networks. In: Proceedings of the 32nd international conference on machine learning, Lille, France, pp 1718–1727

  73. Xu K, Du C, Li C et al (2021) Learning implicit generative models by teaching density estimators. In: Hutter F, Kersting K, Lijffijt J et al (eds.) Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, Cham, pp 239–255. https://doi.org/10.1007/978-3-030-67661-2_15

  74. Sohl-Dickstein J, Weiss E, Maheswaranathan N et al (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: Proceedings of the 32nd international conference on machine learning, Lille, France, pp 2256–2265

  75. Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, [Virtual], pp 840–6851

  76. Croitoru FA, Hondru V, Ionescu RT et al (2023) Diffusion models in vision: a survey. IEEE Trans Pattern Anal Mach Intell 45(9):10850–10869. https://doi.org/10.1631/FITEE.2300310

    Article  Google Scholar 

  77. Lequan L, Li Z, Li R et al (2023) Diffusion models for time-series applications: a survey. Front Inform Technol Electron Eng. https://doi.org/10.1631/FITEE.2300310

    Article  Google Scholar 

  78. Creswell A, White T, Dumoulin V et al (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65. https://doi.org/10.1109/MSP.2017.2765202

    Article  Google Scholar 

  79. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 214–223

  80. Karras T, Laine S, Aittala M et al (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 8107–8116, https://doi.org/10.1109/CVPR42600.2020.00813

  81. Karras T, Aittala M, Laine S et al (2021) Alias-free generative adversarial networks. In: Proceedings of the 35th international conference on neural information processing systems, [Virtual], pp 852–863

  82. Sauer A, Schwarz K, Geiger A (2022) Stylegan-xl: scaling stylegan to large diverse datasets. Proceedings of the ACM SIGGRAPH conference, Vancouver, BC, Canada 49:1–10. https://doi.org/10.1145/3528233.3530738

    Article  Google Scholar 

  83. Sauer A, Karras T, Laine S et al (2023) Stylegan-t: unlocking the power of gans for fast large-scale text-to-image synthesis. https://doi.org/10.48550/arXiv.2301.09515. arXiv:2301.09515v1

  84. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 6000–6010

  85. Li L, Lin Y, Zheng N et al (2017) Parallel learning: a perspective and a framework. IEEE-CAA J Automatica Sin 4(3):389–395. https://doi.org/10.1109/JAS.2017.7510493

    Article  MathSciNet  Google Scholar 

  86. John N (1953) Two-person cooperative games. Econometrica 21(1):128–140. https://doi.org/10.2307/1906951

    Article  MathSciNet  Google Scholar 

  87. Alqahtani H, Kavakli-Thorne M, Kumar G (2021) Applications of generative adversarial networks (gans): an updated review. Arch Computat Methods Eng 28:525–552. https://doi.org/10.1007/s11831-019-09388-y

    Article  MathSciNet  Google Scholar 

  88. Liu M, Wei Y, Wu X et al (2023) Survey on leveraging pre-trained generative adversarial networks for image editing and restoration. Sci China-Inf Sci 66:151101. https://doi.org/10.1007/s11432-022-3679-0

    Article  Google Scholar 

  89. Bau D, Zhu JY, Strobelt H et al (2019) Gan dissection: visualizing and understanding generative adversarial network. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–19, available at https://openreview.net/forum?id=Hyg_X2C5FX

  90. Mirza M, Osindero S (2014) Conditional generative adversarial nets. https://doi.org/10.48550/arXiv.1411.1784. arXiv:1411.1784

  91. Katsumata K, Vo DM, Liu B et al (2024) Revisiting latent space of gan inversion for robust real image editing. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, Waikoloa, Hawaii, pp 5313–5322, available at https://openreview.net/forum?id=Hyg_X2C5FX

  92. Karras T, Laine S, Aila T (2021) A style-based generator architecture for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 43(12):4217–4228. https://doi.org/10.1109/TPAMI.2020.2970919

    Article  Google Scholar 

  93. Zhu P, Abdal R, Qin Y et al (2021) Improved stylegan embedding: where are the good latents?. https://doi.org/10.48550/arXiv.2012.09036. arXiv:2012.09036v3

  94. Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: Proceedings of the 4th international conference on learning representations, San Juan, Puerto Rico, USA, pp 1–16

  95. Spurr A, Aksan E, Hilliges O (2017) Guiding infogan with semi-supervision. In: Ceci M, Hollmén J, Todorovski L et al (eds.) Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, Cham, pp 119–134. https://doi.org/10.1007/978-3-319-71249-9_8

  96. Kurutach T, Tamar A, Yang G et al (2018) Learning plannable representations with causal infogan. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, pp 8747–8758

  97. Reed S, Akata Z, Mohan S et al (2016) Learning what and where to draw. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 217–225

  98. Li X, Mao K, Lin F et al (2023) Feature-aware conditional gan for category text generation. Neurocomputing 547:126352. https://doi.org/10.1016/j.neucom.2023.126352

    Article  Google Scholar 

  99. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 2642–2651

  100. Zhang H, Xu T, Li H et al (2019) Stackgan++: realistic image synthesis with stacked generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(8):1947–1962. https://doi.org/10.1109/TPAMI.2018.2856256

    Article  Google Scholar 

  101. Cha M, Gwon YL, Kung HT (2019) Adversarial learning of semantic relevance in text to image synthesis. In: Proceedings of the AAAI conference on artificial intelligence, Honolulu, Hawaii, USA, pp 3272–3279, https://doi.org/10.1609/aaai.v33i01.33013272

  102. Denton EL, Chintala S, Szlam A et al (2015) Deep generative image models using a laplacian pyramid of adversarial networks. In: Proceedings of the 29th international conference on neural information processing systems, Montréal, Canada, pp 1486–1494

  103. Park H, Yoo Y, Kwak N (2018) Mc-gan: multi-conditional generative adversarial network for image synthesis. In: Proceedings of the British machine vision conference, Newcastle, UK, pp 1–12

  104. Huang X, Li Y, Poursaeed O et al (2017) Stacked generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 1866–1875, https://doi.org/10.1109/CVPR.2017.202

  105. Gauthier J (2014) Conditional generative adversarial nets for convolutional face generation. Project for Stanford CS231N, convolutional Neural Networks for Visual Recognition

  106. Antipov G, Baccouche M, Dugelay JL (2017) Face aging with conditional generative adversarial networks. In: Proceedings of the IEEE international conference on image processing, Beijing, China, pp 2089–2093, https://doi.org/10.1109/ICIP.2017.8296650

  107. Sheng M, Ma Z, Jia H et al (2020) Face aging with conditional generative adversarial network guided by ranking-cnn. In: Proceedings of the IEEE conference on multimedia information processing and retrieval, Shenzhen, China, pp 314–319, https://doi.org/10.1109/MIPR49039.2020.00071

  108. Tang H, Xu D, Sebe N et al (2019) Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 2412–2421. https://doi.org/10.1109/CVPR.2019.00252

  109. Karacan L, Akata Z, Erdem A et al (2016) Learning to generate images of outdoor scenes from attributes and semantic layouts. https://doi.org/10.48550/arXiv.1612.00215. arXiv:1612.00215

  110. Dai B, Fidler S, Urtasun R et al (2017) Towards diverse and natural image descriptions via a conditional gan. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 2989–2998. https://doi.org/10.1109/ICCV.2017.323

  111. Yao S, Hsu TMH, Zhu JY et al (2018) 3d-aware scene manipulation via inverse graphics. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 1891–1902

  112. Chrysos GG, Kossaifi J, Zafeiriou S (2019) Robust conditional generative adversarial networks. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–27, available at https://openreview.net/forum?id=Byg0DsCqYQ

  113. Chrysos GG, Kossaifi J, Zafeiriou S (2020) Rocgan: Robust conditional gan. Int J Comput Vis 128:2665–2683. https://doi.org/10.1007/s11263-020-01348-5

    Article  MathSciNet  Google Scholar 

  114. Thekumparampil KK, Khetan A, Lin Z et al (2018) Robustness of conditional gans to noisy labels. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 10292–10303

  115. Lu Y, Tai YW, Tang CK (2018) Attribute-guided face generation using conditional cyclegan. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) computer vision – ECCV 2018. Springer International Publishing, Cham, pp 293–308. https://doi.org/10.1007/978-3-030-01258-8_18

  116. Mao Q, Lee HY, Tseng HY et al (2019) Mode seeking generative adversarial networks for diverse image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp 1429–1437. https://doi.org/10.1109/CVPR.2019.00152

  117. Gong M, Xu Y, Li C et al (2019) Twin auxiliary classifiers gan. In: Proceedings of the 33rd international conference on neural information processing system, Vancouver, Canada, pp 1328–1337

  118. Deng J, Dong W, Socher R et al (2009) Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Miami, FL, USA, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  119. Isola P, Zhu JY, Zhou T et al (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 5967–5976. https://doi.org/10.1109/CVPR.2017.632

  120. Wang TC, Liu MY, Zhu JY et al (2018) High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 8798–8807. https://doi.org/10.1109/CVPR.2018.00917

  121. Donahue C, Lipton ZC, Balsubramani A et al (2018) Semantically decomposing the latent spaces of generative adversarial networks. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–19, available at https://openreview.net/forum?id=S1nQvfgA-

  122. Xu X, Li Y, Yuan C (2021) Conditional image generation with one-vs-all classifier. Neurocomputing 434:261–267. https://doi.org/10.1016/j.neucom.2020.12.091

    Article  Google Scholar 

  123. Wang X, Xu G, Wang Y et al (2019) Thin and thick cloud removal on remote sensing image by conditional generative adversarial network. In: Proceedings of the IEEE international geoscience and remote sensing symposium, Yokohama, Japan, pp 1426–1429. https://doi.org/10.1109/IGARSS.2019.8897958

  124. Zhou G, Fan Y, Shi J et al (2022) Conditional generative adversarial networks for domain transfer: a survey. Appl Sci 12(16):8350. https://doi.org/10.3390/app12168350

    Article  Google Scholar 

  125. Perarnau G, van de Weijer J, Raducanu B et al (2016) Invertible conditional gans for image editing. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain

  126. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: Leibe B, Matas J, Sebe N et al (eds) computer vision – ECCV 2016. Springer International Publishing, Cham, pp 694–711. https://doi.org/10.1007/978-3-319-46475-6_43

  127. Li R, Cao W, Jiao Q et al (2020) Simplified unsupervised image translation for semantic segmentation adaptation. Pattern Recognit 105:107343. https://doi.org/10.1016/j.patcog.2020.107343

    Article  Google Scholar 

  128. Li H, Wei P, Hu P (2022) Avn: an adversarial variation network model for handwritten signature verification. IEEE Trans Multimedia 24:594–608. https://doi.org/10.1109/TMM.2021.3056217

    Article  Google Scholar 

  129. Yuan Y, Liu S, Zhang J et al (2018) Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Salt Lake City, UT, USA, pp 814–823. https://doi.org/10.1109/CVPRW.2018.00113

  130. Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 2242–2251. https://doi.org/10.1109/ICCV.2017.244

  131. Kim T, Cha M, Kim H et al (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 1857–1865

  132. Yi Z, Zhang H, Tan P et al (2017) Dualgan: unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp 2868–2876. https://doi.org/10.1109/ICCV.2017.310

  133. Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 700–708

  134. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 469–477

  135. Kaneko T, Kameoka H (2018) Cyclegan-vc: non-parallel voice conversion using cycle-consistent adversarial networks. In: Proceedings of the 26th European signal processing conference, Rome, Italy, pp 2100–2104. https://doi.org/10.23919/EUSIPCO.2018.8553236

  136. Kaneko T, Kameoka H, Tanaka K et al (2019) Cyclegan-vc2: improved cyclegan-based non-parallel voice conversion. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Brighton, UK, pp 6820–6824. https://doi.org/10.1109/ICASSP.2019.8682897

  137. Li M, Huang H, Ma L et al (2018) Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 186–201. https://doi.org/10.1007/978-3-030-01240-3_12

  138. Benaim S, Wolf L (2018) One-shot unsupervised cross domain translation. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 2108–2118

  139. Cohen T, Wolf L (2019) Bidirectional one-shot unsupervised domain mapping. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, pp 1784–1792. https://doi.org/10.1109/ICCV.2019.00187

  140. Wang J, Jiang J (2019) Conditional coupled generative adversarial networks for zero-shot domain adaptation. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, pp 3374–3383. https://doi.org/10.1109/ICCV.2019.00347

  141. He J, Wang C, Jiang D et al (2020) Cyclegan with an improved loss function for cell detection using partly labeled images. IEEE J Biomed Health Inform 24(9):2473–2480. https://doi.org/10.1109/JBHI.2020.2970091

    Article  Google Scholar 

  142. Sandfort V, Yan K, Pickhardt PJ et al (2019) Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in ct segmentation tasks. Sci Rep 9(1):16884. https://doi.org/10.1038/s41598-019-52737-x

    Article  Google Scholar 

  143. Wang L, Wang L, Chen S (2022) Esa-cyclegan: edge feature and self-attention based cycle-consistent generative adversarial network for style transfer. IET Image Process 16(1):176–190. https://doi.org/10.1049/ipr2.12342

    Article  Google Scholar 

  144. Chang B, Zhang Q, Pan S et al (2018) Generating handwritten chinese characters using cyclegan. In: Proceedings of the IEEE winter conference on applications of computer vision, Lake Tahoe, NV, USA, pp 199–207. https://doi.org/10.1109/WACV.2018.00028

  145. Chen H, Guan M, Li H (2021) Arcyclegan: improved cyclegan for style transferring of fruit images. IEEE Access 9:46776–46787. https://doi.org/10.1109/ACCESS.2021.3068094

    Article  Google Scholar 

  146. Hoyez H, Schockaert C, Rambach J et al (2022) Unsupervised image-to-image translation: A review. Sensor 22(21):8540. https://doi.org/10.3390/s22218540

    Article  Google Scholar 

  147. Choi Y, Choi M, Kim M et al (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Salt Lake City, UT, USA, pp 8789–8797. https://doi.org/10.1109/CVPR.2018.00916

  148. Huang X, Liu MY, Belongie S et al (2018) Multimodal unsupervised image-to-image translation. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 179–196. https://doi.org/10.1007/978-3-030-01219-9_11

  149. Almahairi A, Rajeswar S, Sordoni A et al (2018) Augmented cyclegan: learning many-to-many mappings from unpaired data. In: Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, pp 195–204

  150. Anoosheh A, Agustsson E, Timofte R et al (2018) Combogan: unrestrained scalability for image domain translation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Salt Lake City, UT, USA, pp 896–903. https://doi.org/10.1109/CVPRW.2018.00122

  151. Mo S, Cho M, Shin J (2019) Instagan: instance-aware image-to-image translation. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–26, available at https://openreview.net/forum?id=ryxwJhC9YX

  152. Lin J, Xia Y, Wang Y et al (2019) Image-to-image translation with multi-path consistency regularization. In: Proceedings of the 28th international joint conference on artificial intelligence, Macao, China, pp 2980–2986. https://doi.org/10.24963/ijcai.2019/413

  153. Zhang C, Xi W, Liu X et al (2022) Unsupervised multimodal image-to-image translation: Generate what you want. In: Proceedings of the international joint conference on neural networks, Padua, Italy, pp 1–8. https://doi.org/10.1109/IJCNN55064.2022.9892018

  154. Tiao LC, Bonilla EV, Ramos F (2018) Cycle-consistent adversarial learning as approximate bayesian inference. In: Proceedings of the 35th ICML workshop on theoretical foundations and applications of deep generative models, Stockholm, Sweden

  155. Villani C (2009) Cyclical monotonicity and kantorovich duality. In: Optimal Transport: old and New, vol 338. Springer Berlin Heidelberg, Berlin, Heidelberg, p 51–92. https://doi.org/10.1007/978-3-540-71050-9_5

  156. Gulrajani I, Ahmed F, Arjovsky M et al (2017) Improved training of wasserstein gans. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 5769–5779

  157. Miyato T, Kataoka T, Koyama M et al (2018) Spectral normalization for generative adversarial networks. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–26, available at https://openreview.net/forum?id=B1QRgziT-

  158. Liu K, Qiu G (2020) Lipschitz constrained gans via boundedness and continuity. Neural Comput Appl 32:18271–18283. https://doi.org/10.1007/s00521-020-04954-z

    Article  Google Scholar 

  159. Petzka H, Fischer A, Lukovnikov D (2018) On the regularization of wasserstein gans. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–24, available at https://openreview.net/forum?id=B1hYRMbCW

  160. Roth K, Lucchi A, Nowozin S et al (2017) Stabilizing training of generative adversarial networks through regularization. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 2015–2025

  161. Qi GJ (2020) Loss-sensitive generative adversarial networks on lipschitz densities. Int J Comput Vision 128(5):1118–1140. https://doi.org/10.1007/s11263-019-01265-2

    Article  MathSciNet  Google Scholar 

  162. Mroueh Y, Sercu T (2017) Fisher gan. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 2510–2520

  163. Mroueh Y, Li CL, Sercu T et al (2018) Sobolev gan. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–27, available at https://openreview.net/forum?id=SJA7xfb0b

  164. Wei X, Gong B, Liu Z et al (2018) Improving the improved training of wasserstein gans: a consistency term and its dual effect. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–17, available at https://openreview.net/forum?id=SJx9GQb0-

  165. Wu J, Huang Z, Thoma J et al (2018) Wasserstein divergence for gans. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 673–688. https://doi.org/10.1007/978-3-030-01228-1_40

  166. Bellemare MG, Danihelka I, Dabney W et al (2017) The cramer distance as a solution to biased wasserstein gradients. https://doi.org/10.48550/arXiv.1705.10743. arXiv:1705.10743

  167. Wu J, Zhang C, Xue T et al (2016) Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 82–90

  168. Gregor K, Danihelka I, Graves A et al (2015) Draw: a recurrent neural network for image generation. In: Proceedings of the 32nd international conference on machine learning, Lille, France, pp 1462–1471

  169. Zhang H, Goodfellow I, Metaxas D et al (2019) Self-attention generative adversarial networks. In: Proceedings of the 36th international conference on machine learning, Long Beach, California, USA, pp 17354–7363

  170. Xu T, Zhang P, Huang Q et al (2018) Attngan: fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 1316–1324. https://doi.org/10.1109/CVPR.2018.00143

  171. Tran D, Ranganath R, Blei DM (2017) Hierarchical implicit models and likelihood-free variational inference. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 5523–5533

  172. Lim JH, Ye JC (2017) Geometric gan. https://doi.org/10.48550/arXiv.1705.02894. arXiv:1705.02894

  173. Brock A, Donahue J, Simonyan K (2019) Large scale gan training for high fidelity natural image synthesis. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–35, available at https://openreview.net/forum?id=B1xsqj09Fm

  174. Jeha P, Bohlke-Schneider M, Mercado P et al (2022) Psa-gan: progressive self attention gans for synthetic time series. In: Proceedings of the 10th international conference on learning representations, [Virtual], pp 1–20, available at https://openreview.net/forum?id=Ix_mh42xq5w

  175. Zhang L, Wu J, Shen J et al (2021) Satp-gan: self-attention based generative adversarial network for traffic flow prediction. Transportmetrica B-Transp Dyn 9(1):552–56. https://doi.org/10.1080/21680566.2021.1916646

    Article  Google Scholar 

  176. Kodali N, Abernethy J, Hays J et al (2017) On convergence and stability of gans. https://doi.org/10.48550/arXiv.1705.07215. arXiv:1705.07215v5

  177. Metz L, Poole B, Pfau D et al (2017) Unrolled generative adversarial networks. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–25, available at https://openreview.net/forum?id=BydrOIcle

  178. Armandpour M, Sadeghian A, Li C et al (2021) Partition-guided gans. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Nashville, TN, USA, pp 5095–5105. https://doi.org/10.1109/CVPR46437.2021.00506

  179. Hwang U, Kim H, Jung D et al (2022) Stein latent optimization for generative adversarial networks. In: Proceedings of the 10th international conference on learning representations, [Virtual], pp 1–35, available at https://openreview.net/forum?id=2-mkiUs9Jx7

  180. Chen J, Wang WH, Gao H et al (2021) Improving the generalization of generative adversarial networks against membership inference attacks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, Virtual, pp 127–137. https://doi.org/10.1145/3447548.3467445

  181. Van Gansbeke W, Vandenhende S, Georgoulis S et al (2020) Scan: Learning to classify images without labels. In: Vedaldi A, Bischof H, Brox T et al (eds) Computer Vision – ECCV 2020. Springer International Publishing, Cham, pp 268–285. https://doi.org/10.1007/978-3-030-58607-2_16

  182. Karras T, Aila T, Laine S et al (2018) Progressive growing of gans for improved quality, stability, and variation. In: Proceedings of the 6th International Conference on Learning Representations, Vancouver, BC, Canada, pp 1–26, available at https://openreview.net/forum?id=Hk99zCeAb

  183. Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 4396–4405. https://doi.org/10.1109/CVPR.2019.00453

  184. Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 1510–1519. https://doi.org/10.1109/ICCV.2017.167

  185. Karras T, Aittala M, Hellsten J et al (2020) Training generative adversarial networks with limited data. In: Proceedings of the 34th international conference on neural information processing systems, [Virtual], pp 12104–12114

  186. Kang M, Shin J, Park J (2023) Studiogan: A taxonomy and benchmark of gans for image synthesis. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2023.3306436

    Article  Google Scholar 

  187. Micikevicius P, Narang S, Alben J et al (2018) Mixed precision training. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–12, available at https://openreview.net/forum?id=r1gs9JgRZ

  188. Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the 9th international conference on learning representations, [Virtual], pp 1–21, available at https://openreview.net/forum?id=YicbFdNTTy

  189. Jiang Y, Chang S, Wang Z (2021) Transgan: two pure transformers can make one strong gan, and that can scale up. In: Proceedings of the 35th international conference on neural information processing systems, [Virtual], pp 14745–14758

  190. Zhang B, Gu S, Zhang B et al (2022) Styleswin: transformer-based gan for high-resolution image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, pp 11294–11304. https://doi.org/10.1109/CVPR52688.2022.01102

  191. Hudson DA, Zitnick L (2021) Generative adversarial transformers. In: Proceedings of the 38th international conference on machine learning, [Virtual], pp 4487–4499

  192. Xu R, Xu X, Chen K et al (2023) The nuts and bolts of adopting transformer in gans. https://doi.org/10.48550/arXiv.2110.13107. arXiv:2110.13107v3

  193. Shin AH, Kim ST, Park GM (2023) Time series anomaly detection using transformer-based gan with two-step masking. IEEE Access 11:74035–74047. https://doi.org/10.1109/ACCESS.2023.3289921

    Article  Google Scholar 

  194. Li X, Metsis V, Wang H et al (2022) Tts-gan: a transformer-based time-series generative adversarial network. In: Michalowski M, Abidi SSR, Abidi S (eds) Artificial Intelligence in Medicine. Springer International Publishing, Cham, pp 133–143. https://doi.org/10.1007/978-3-031-09342-5_13

  195. Hindupur A (2023) The gan zoo. https://github.com/hindupuravinash/the-gan-zoo, accessed: 28 March 2023

  196. Li Y, Schwing A, Wang KC et al (2017) Dualing gans. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp 5606–5616

  197. Zhou Z, Liang J, Song Y et al (2019) Lipschitz generative adversarial nets. In: Proceedings of the 36th international conference on machine learning, Long Beach, California, USA, pp 7584–7593

  198. Thanh-Tung H, Tran T, Venkatesh S (2019) Improving generalization and stability of generative adversarial networks. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–18, available at https://openreview.net/forum?id=ByxPYjC5KQ

  199. Daskalakis C, Panageas I (2018) The limit points of (optimistic) gradient descent in min-max optimization. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 9256–9266

  200. Daskalakis C, Ilyas A, Syrgkanis V et al (2018) Training gans with optimism. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–26, available at https://openreview.net/forum?id=SJJySbbAZ

  201. Mazumdar E, Jordan MI, Sastry SS (2019) On finding local nash equilibria (and only local nash equilibria) in zero-sum games. https://doi.org/10.48550/arXiv.1901.00838. arXiv:1901.00838

  202. Wang Y, Ma X, Bailey J et al (2019) On the convergence and robustness of adversarial training. In: Proceedings of the 36th international conference on machine learning, Long Beach, California, USA, pp 6586–6595

  203. Lin T, Jin C, Jordan MI (2020) On gradient descent ascent for nonconvex-concave minimax problems. In: Proceedings of the 37th international conference on machine learning, [Virtual], pp 6083–6093

  204. Hsieh YP, Liu C, Cevher V (2019) Finding mixed nash equilibria of generative adversarial networks. In: Proceedings of the 36th international conference on machine learning, Long Beach, California, USA, pp 2810–2819

  205. Nagarajan V, Kolter JZ (2017) Gradient descent gan optimization is locally stable. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 5591–5600

  206. Mertikopoulos P, Papadimitriou C, Piliouras G (2018) Cycles in adversarial regularized learning. In: Proceedings of the 2018 annual ACM-SIAM symposium on discrete algorithms, New Orleans, Louisiana, USA, pp 2703–2717, https://doi.org/10.1137/1.9781611975031.172

  207. Hazan E, Singh K, Zhang C (2017) Efficient regret minimization in non-convex games. In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 1433–1441

  208. Berard H, Gidel G, Almahairi A et al (2020) A closer look at the optimization landscapes of generative adversarial networks. In: Proceedings of the 8th international conference on learning representations, [Virtual], pp 1–18, available at https://openreview.net/forum?id=HJeVnCEKwH

  209. Arora S, Ge R, Liang Y et al (2017) Generalization and equilibrium in generative adversarial nets (gans). In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 224–232

  210. Mao X, Li Q, Xie H et al (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 2813–2821, https://doi.org/10.1109/ICCV.2017.304

  211. LeCun YA, Bottou L, Orr GB et al (2012) Efficient backprop, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 9–48. https://doi.org/10.1007/978-3-642-35289-8_3

  212. Tran NT, Bui TA, Cheung NM (2018) Dist-gan: an improved gan using distance constraints. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 387–401. https://doi.org/10.1007/978-3-030-01264-9_23

  213. Karnewar A, Wang O (2020) Msg-gan: multi-scale gradients for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 7796–7805. https://doi.org/10.1109/CVPR42600.2020.00782

  214. Berthelot D, Schumm T, Metz L (2017) Began: boundary equilibrium generative adversarial networks. https://doi.org/10.48550/arXiv.1703.10717. arXiv:1703.10717v4

  215. Che T, Li Y, Jacob AP et al (2017) Mode regularized generative adversarial networks. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–23, available at https://openreview.net/forum?id=HJKkY35le

  216. Lin Z, Khetan A, Fanti G et al (2020) Pacgan: the power of two samples in generative adversarial networks. IEEE J Sel Areas Inf Th 1(1):324–335. https://doi.org/10.1109/JSAIT.2020.2983071

    Article  Google Scholar 

  217. Xiao C, Zhong P, Zheng C (2018) Bourgan: generative networks with metric embeddings. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 2275–2286

  218. Khayatkhoei M, Elgammal A, Singh M (2018) Disconnected manifold learning for generative adversarial networks. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 7354–7364

  219. Thanh-Tung H, Tran T (2020) Catastrophic forgetting and mode collapse in gans. In: Proceedings of the international joint conference on neural networks, Glasgow, UK, pp 1–10, https://doi.org/10.1109/IJCNN48605.2020.9207181

  220. Chu C, Minami K, Fukumizu K (2020) Smoothness and stability in gans. In: Proceedings of the 8th international conference on learning representations, [Virtual], pp 1–31, available at https://openreview.net/forum?id=HJeOekHKwr

  221. Than K, Vu N (2021) Generalization and stability of gans: a theory and promise from data augmentation. https://paperswithcode.com/paper/generalization-and-stability-of-gans-a-theory. Accessed 15 May 2023

  222. Shrivastava A, Pfister T, Tuzel O et al (2017) Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 2242–2251. https://doi.org/10.1109/CVPR.2017.241

  223. Ian G (2016) Nips 2016 tutorial: Generative adversarial networks. https://doi.org/10.48550/arXiv.1701.00160. arXiv:1701.00160v4

  224. Barnett SA (2018) Convergence problems with generative adversarial networks (gans). https://doi.org/10.48550/arXiv.1806.11382. arXiv:1806.11382

  225. Zhang H, Xu S, Jiao J et al (2018) Stackelberg gan: towards provable minimax equilibrium via multi-generator architectures. https://doi.org/10.48550/arXiv.1811.08010. arXiv:1811.08010v1

  226. Oliehoek FA, Savani R, Gallego J et al (2019) Beyond local nash equilibria for adversarial networks. In: Atzmueller M, Duivesteijn W (eds) Artificial Intelligence. Springer International Publishing, Cham, pp 73–89. https://doi.org/10.1007/978-3-030-31978-6_7

  227. Franci B, Grammatico S (2022) Stochastic generalized nash equilibrium seeking under partial-decision information. Automatic 137:110101. https://doi.org/10.1016/j.automatica.2021.110101

    Article  MathSciNet  Google Scholar 

  228. Shannon M, Poole B, Mariooryad S et al (2020) Non-saturating gan training as divergence minimization. https://doi.org/10.48550/arXiv.2010.08029. arXiv:2010.08029

  229. Fedus W, Rosca M, Lakshminarayanan B et al (2018) Many paths to equilibrium: Gans do not need to decrease a divergence at every step. In: Proceedings of the 6th international conference on learning representations, Vancouver, BC, Canada, pp 1–21, available at https://openreview.net/pdf?id=ByQpn1ZA-

  230. Glicksberg IL (1952) A further generalization of the kakutani fixed point theorem, with application to nash equilibrium points. Proc Amer Math Soc 3(1):170–174. https://doi.org/10.2307/2032478

    Article  MathSciNet  Google Scholar 

  231. Nash JF (1950) Equilibrium points in n-person games. Proc Natl Acad Sci USA 36(1):48–49. https://doi.org/10.1073/pnas.36.1.48

    Article  MathSciNet  Google Scholar 

  232. Xu K, Li C, Zhu J et al (2020) Understanding and stabilizing gans’ training training dynamics using control theory. In: Proceedings of the 37th international conference on machine learning, [Virtual], pp 10566–10575

  233. Mu J, Xin M, Li S et al (2023) An automatic control perspective on parameterizing generative adversarial network. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2023.3267773

    Article  Google Scholar 

  234. Kailath T (1980) Linear systems. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  235. Heusel M, Ramsauer H, Unterthiner T et al (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 6629–6640

  236. Diviya M, Karmel A (2023) Tam gan: tamil text to naturalistic image synthesis using conventional deep adversarial networks. ACM Trans Asian Low-Resour Lang Inf Process 22(5):128. https://doi.org/10.1145/3584019

    Article  Google Scholar 

  237. Saxena D, Cao J (2021) Generative adversarial networks (gans): challenges, solutions, and future directions. ACM Comput Surv 54(3):63. https://doi.org/10.1145/3446374

    Article  Google Scholar 

  238. Brown GW (1951) Iterative solutions of games by fictitious play. In: Koopmans TC (ed) Activity Analysis of Production and Allocation, vol 13. Wiley, NewYork, pp 374–376

    Google Scholar 

  239. Ge H, Xia Y, Chen X et al (2018) Fictitious gan: training gans with historical models. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 122–137. https://doi.org/10.1007/978-3-030-01246-5_8

  240. Kim Y, Kim M, Kim G (2018) Memorization precedes generation: learning unsupervised gans with memory networks. In: Proceedings of the 6th International Conference on Learning Representations, Vancouver, BC, Canada, pp 1–15, available at https://openreview.net/forum?id=rkO3uTkAZ

  241. Yazıcı Y, Foo CS, Winkler S et al (2019) The unusual effectiveness of averaging in gan training. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–22, available at https://openreview.net/forum?id=SJgw_sRqFQ

  242. David WF, Goodfellow I (2017) Adversarial perturbations of deep neural networks, MIT Press, Cambridge, MA, pp 311–342. https://doi.org/10.7551/mitpress/10761.003.0012

  243. Kurach K, Lučić M, Zhai X et al (2019) A large-scale study on regularization and normalization in gans. In: Proceedings of the 36th international conference on machine learning, Long Beach, California, USA, pp 3581–3590

  244. Li Z, Usman M, Tao R et al (2023) A systematic survey of regularization and normalization in gans. ACM Comput Surv 55(11):232. https://doi.org/10.1145/3569928

    Article  Google Scholar 

  245. Zhang H, Zhang Z, Odena A et al (2020) Consistency regularization for generative adversarial networks. In: Proceedings of the 8th international conference on learning representations, [Virtual], pp 1–19, available at https://openreview.net/forum?id=S1lxKlSKPH

  246. Zhao Z, Singh S, Lee H et al (2021) Improved consistency regularization for gans. Proceedings of the AAAI conference on artificial intelligence 35(12):11033–11041. https://doi.org/10.1609/aaai.v35i12.17317

  247. Adler J, Lunz S (2018) Banach wasserstein gan. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 6755–6764

  248. Brock A, Lim T, Ritchie JM et al (2017) Neural photo editing with introspective adversarial networks. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–15, available at https://openreview.net/pdf?id=HkNKFiGex

  249. Nie W, Patel A (2019) Towards a better understanding and regularization of gan training dynamics. In: Proceedings of the 35th conference on uncertainty in artificial intelligence, Tel Aviv, Israel, pp 281–291

  250. Tran NT, Tran VH, Nguyen NB et al (2021) On data augmentation for gan training. IEEE Trans Image Process 30:1882–1897. https://doi.org/10.1109/TIP.2021.3049346

    Article  MathSciNet  Google Scholar 

  251. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning, Lille, France, pp 448–456

  252. Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. https://doi.org/10.48550/arXiv.1607.06450. arXiv:1607.06450v1

  253. Wu YL, Shuai HH, Tam ZR et al (2021) Gradient normalization for generative adversarial networks. In: Proceedings of the IEEE/CVF international conference on computer vision, Montréal, Canada, pp 6353–6362, https://doi.org/10.1109/ICCV48922.2021.00631

  254. Kligvasser I, Michaeli T (2021) Sparsity aware normalization for gans. Proceedings of the AAAI conference on artificial intelligence 35(9):8181–8190. https://doi.org/10.1609/aaai.v35i9.16996

  255. Ulyanov D, Vedaldi A, Lempitsky V (2020) Instance normalization: the missing ingredient for fast stylization. https://doi.org/10.48550/arXiv.1607.08022. arXiv:1607.08022v3

  256. Wu Y, He K (2020) Group normalization. Int J Comput Vis 128:742–755. https://doi.org/10.1007/s11263-019-01198-w

    Article  Google Scholar 

  257. Salimans T, Kingma DP (2016) Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 901–909

  258. Donahue J, Krähenbühl P, Darrell T (2017) Adversarial feature learning. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–18, available at https://openreview.net/forum?id=BJtNZAFgg

  259. Daras G, Odena A, Zhang H et al (2020) Your local gan: designing two dimensional local attention mechanisms for generative models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 14519–14527. https://doi.org/10.1109/CVPR42600.2020.01454

  260. Wang H, Huan J (2019) Agan: towards automated design of generative adversarial networks.https://doi.org/10.48550/arXiv.1906.11080. arXiv:1906.11080

  261. Gong X, Chang S, Jiang Y et al (2019) Autogan: neural architecture search for generative adversarial networks. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, pp 3223–3233, https://doi.org/10.1109/ICCV.2019.00332

  262. Tian Y, Wang Q, Huang Z et al (2020) Off-policy reinforcement learning for efficient and effective gan architecture search. In: Vedaldi A, Bischof H, Brox T et al (eds) Computer Vision–ECCV 2020. Springer International Publishing, Cham, pp 175–192. https://doi.org/10.1007/978-3-030-58571-6_11

  263. Fan Y, Tang X, Zhou G et al (2022) Efficientautogan: predicting the rewards in reinforcement-based neural architecture search for generative adversarial networks. IEEE Trans Cogn Dev Syst 14(1):234–245. https://doi.org/10.1109/TCDS.2020.3040796

    Article  Google Scholar 

  264. Fan Y, Zhou G, Shen J et al (2021) Toward gradient bandit-based selection of candidate architectures in autogan. Soft Comput 25:4367–4378. https://doi.org/10.1007/s00500-020-05446-x

    Article  Google Scholar 

  265. Fan Y, Zhou Q, Zhang W et al (2021) Determining learning direction via multi-controller model for stably searching generative adversarial networks. Neurocomputing 464:37–47. https://doi.org/10.1016/j.neucom.2021.08.070

    Article  Google Scholar 

  266. Shi J, Zhou G, Bao S et al (2023) Multiselfgan: a self-guiding neural architecture search method for generative adversarial networks with multicontrollers. IEEE Trans Cogn Dev Syst 15(2):544–554. https://doi.org/10.1109/TCDS.2022.3160475

    Article  Google Scholar 

  267. Shi J, Fan Y, Zhou G et al (2022) Distributed gan: toward a faster reinforcement-learning-based architecture search. IEEE Trans Artif Intell 3(3):391–401. https://doi.org/10.1109/TAI.2021.3133509

    Article  Google Scholar 

  268. Gao C, Chen Y, Liu S et al (2020) Adversarialnas: adversarial neural architecture search for gans. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 5679–5688. https://doi.org/10.1109/CVPR42600.2020.00572

  269. Fu Y, Chen W, Wang H et al (2020) Autogan-distiller: searching to compress generative adversarial networks. In: Proceedings of the 37th international conference on machine learning, [Virtual], pp 3292–3303

  270. Doveh S, Giryes R (2021) Degas: differentiable efficient generator search. Neural Comput Appl 33:17173–17184. https://doi.org/10.1007/s00521-021-06309-8

    Article  Google Scholar 

  271. Tian Y, Shen L, Shen L et al (2022) Alphagan: fully differentiable architecture search for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 44(10):6752–6766. https://doi.org/10.1109/TPAMI.2021.3099829

    Article  Google Scholar 

  272. Lin Q, Fang Z, Chen Y et al (2022) Evolutionary architectural search for generative adversarial networks. IEEE Trans Emerg Top Comput Intell 6(4):783–794. https://doi.org/10.1109/TETCI.2021.3137377

    Article  Google Scholar 

  273. Kobayashi M, Nagao T (2020) A multi-objective architecture search for generative adversarial networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, [Virtual], pp 133–134. https://doi.org/10.1145/3377929.3390004

  274. Ying G, He X, Gao B et al (2022) Eagan: efficient two-stage evolutionary architecture search for gans. In: Avidan S, Brostow G, Cissé M et al (eds) Computer Vision – ECCV 2022. Springer Nature Switzerland, Cham, pp 37–53. https://doi.org/10.1007/978-3-031-19787-1_3

  275. Sajjadi MSM, Bachem O, Lucic M et al (2018) Assessing generative models via precision and recall. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 5234–5243

  276. Liu S, Wei Y, Lu J et al (2020) An improved evaluation framework for generative adversarial networks. https://doi.org/10.48550/arXiv.1803.07474. arXiv:1803.07474

  277. Lucic M, Kurach K, Michalski M et al (2018) Are gans created equal? a large-scale study. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 698–707

  278. Shmelkov K, Schmid C, Alahari K (2018) How good is my gan? In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 218–234, https://doi.org/10.1007/978-3-030-01216-8_14

  279. Xu Q, Huang G, Yuan Y et al (2018) An empirical study on evaluation metrics of generative adversarial networks. https://doi.org/10.48550/arXiv.1806.07755. arXiv:1806.07755

  280. Gurumurthy S, Sarvadevabhatla RK, Babu RV (2017) Deligan: generative adversarial networks for diverse and limited data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 4941–4949. https://doi.org/10.1109/CVPR.2017.525

  281. Naeem MF, Oh SJ, Uh Y et al (2020) Reliable fidelity and diversity metrics for generative models. In: Proceedings of the 37th international conference on machine learning, [Virtual], pp 7176–7185

  282. Alaa AM, Breugel Bv, Saveliev E et al (2022) How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models. In: Proceedings of the 39th international conference on machine learning, Baltimore, Maryland, USA, pp 7290–306

  283. Srivastava A, Valkoz L, Russell C et al (2017) Veegan: reducing mode collapse in gans using implicit variational learning. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 3310–3320

  284. Santurkar S, Schmidt L, Madry A (2018) A classification-based study of covariate shift in gan distributions. In: Proceedings of the 35th international conference on machine learning, Stockholm, Sweden, pp 4480–4489

  285. Ledig C, Theis L, Huszar F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 105–114, https://doi.org/10.1109/CVPR.2017.19

  286. Wang X, Yu K, Wu S et al (2019) Esrgan: enhanced super-resolution generative adversarial networks. In: Leal-Taixé L, Roth S (eds) Computer Vision – ECCV 2018 Workshops. Springer International Publishing, Cham, pp 63–79. https://doi.org/10.1007/978-3-030-11021-5_5

  287. Guan J, Pan C, Li S et al (2019) Srdgan: learning the noise prior for super resolution with dual generative adversarial networks. https://doi.org/10.48550/arXiv.1903.11821. arXiv:1903.11821v1

  288. Ding Z, Liu XY, Yin M et al (2019) Tgan: deep tensor generative adversarial nets for large image generation. https://doi.org/10.48550/arXiv.1901.09953. arXiv:1901.09953v2

  289. Zhang Y, Liu S, Dong C et al (2020) Multiple cycle-in-cycle generative adversarial networks for unsupervised image super-resolution. IEEE Trans Image Process 29:1101–1112. https://doi.org/10.1109/TIP.2019.2938347

    Article  MathSciNet  Google Scholar 

  290. Yu X, Porikli F (2016) Ultra-resolving face images by discriminative generative networks. In: Leibe B, Matas J, Sebe N et al (eds) Computer Vision – ECCV 2016. Springer International Publishing, Cham, pp 318–333, https://doi.org/10.1007/978-3-319-46454-1_20

  291. Zhu H, Huang H, Li Y et al (2020) Arbitrary talking face generation via attentional audio-visual coherence learning. https://doi.org/10.48550/arXiv.1812.06589. arXiv:1812.06589v2

  292. Jolicoeur-Martineau A (2019) The relativistic discriminator: a key element missing from standard gan. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–26, available at https://openreview.net/pdf?id=S1erHoR5t7

  293. Yan Y, Liu C, Chen C et al (2022) Fine-grained attention and feature-sharing generative adversarial networks for single image super-resolution. IEEE Trans Multimedia 24:1473–1487. https://doi.org/10.1109/TMM.2021.3065731

    Article  Google Scholar 

  294. Huang H, He R, Sun Z et al (2019) Wavelet domain generative adversarial network for multi-scale face hallucination. Int J Comput Vis 127:763–784. https://doi.org/10.1007/s11263-019-01154-8

    Article  Google Scholar 

  295. Sønderby CK, Caballero J, Theis L et al (2017) Amortised map inference for image super-resolution. In: Proceedings of the 5th International Conference on Learning Representations, Toulon, France, pp 1–17, available at https://openreview.net/forum?id=S1RP6GLle

  296. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: Leibe B, Matas J, Sebe N et al (eds) Computer Vision – ECCV 2016. Springer International Publishing, Cham, pp 694–711. https://doi.org/10.1007/978-3-319-46475-6_43

  297. Zhang W, Liu Y, Dong C et al (2019) Ranksrgan: generative adversarial networks with ranker for image super-resolution. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, pp 3096–3105. https://doi.org/10.1109/ICCV.2019.00319

  298. Wang X, Yu K, Dong C et al (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 606–615. https://doi.org/10.1109/CVPR.2018.00070

  299. Farooq MA, Yao W, Costache G et al (2023) Childgan: large scale synthetic child facial data using domain adaptation in stylegan. IEEE Access 11:108775–108791. https://doi.org/10.1109/ACCESS.2023.3321149

    Article  Google Scholar 

  300. Zhang Z, Pan X, Jiang S et al (2020) High-quality face image generation based on generative adversarial networks. J Vis Commun Image Represent 71:102719. https://doi.org/10.1016/j.jvcir.2019.102719

    Article  Google Scholar 

  301. Ma L, Jia X, Sun Q et al (2017) Pose guided person image generation. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 406–416

  302. Siarohin A, Lathuilière S, Sangineto E, et al (2021) Appearance and pose-conditioned human image generation using deformable gans. IEEE Trans Pattern Anal Mach Intell 43(4):1156–1171. https://doi.org/10.1109/TPAMI.2019.2947427

  303. Tran L, Yin X, Liu X (2017) Disentangled representation learning gan for pose-invariant face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp 1283–1292. https://doi.org/10.1109/CVPR.2017.141

  304. Wu J, Wang J, Si S et al (2022) Pose guided human image synthesis with partially decoupled gan. In: Proceedings of the 14th Asian conference on machine learning, Hyderabad, India, PMLR 189, pp 1133–1148

  305. Yi R, Liu YJ, Lai YK et al (2019) Apdrawinggan: generating artistic portrait drawings from face photos with hierarchical gans. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 10735–10744. https://doi.org/10.1109/CVPR.2019.01100

  306. Liu Q, Zhao H, Wang Y et al (2021) Sketch to portrait generation with generative adversarial networks and edge constraint. Comput Electr Eng 95:107338. https://doi.org/10.1016/j.compeleceng.2021.107338

    Article  Google Scholar 

  307. Gu S, Bao J, Yang H et al (2019) Mask-guided portrait editing with conditional gans. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 3431–3440. https://doi.org/10.1109/CVPR.2019.00355

  308. Chen J, Liu G, Yuan G et al (2022) Portrait sketch synthesis via mish-gated u-net and gans. In: Proceedings of the 5th international conference on machine learning and natural language processing, Sanya, China, pp 59–62. https://doi.org/10.1145/3578741.3578753

  309. Rosado P, Fernandez R, Reverter F (2021) Gans and artificial facial expressions in synthetic portraits. Big Data Cogn Comput 5(4):63. https://doi.org/10.3390/bdcc5040063

    Article  Google Scholar 

  310. Chang H, Lu J, Yu F et al (2018) Pairedcyclegan: asymmetric style transfer for applying and removing makeup. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 40–48. https://doi.org/10.1109/CVPR.2018.00012

  311. Huang R, Zhang S, Li T et al (2017) Beyond face rotation: global and local perception gan for photorealistic and identity preserving frontal view synthesis. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 2458–2467, https://doi.org/10.1109/ICCV.2017.267

  312. Li C, Huang Z (2019) An improved face synthesis model for two-pathway generative adversarial network. In: Proceedings of the 11th international conference on machine learning and computing, Zhuhai, China, pp 434–438. https://doi.org/10.1145/3318299.3318346

  313. Yin X, Yu X, Sohn K et al (2017) Towards large-pose face frontalization in the wild. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 4010–4019. https://doi.org/10.1109/ICCV.2017.430

  314. Zhuang W, Chen L, Hong C et al (2019) Ft-gan: face transformation with key points alignment for pose-invariant face recognition. Electronics 8(7):807. https://doi.org/10.3390/electronics8070807

    Article  Google Scholar 

  315. Liu Y, Chen J (2022) Unsupervised face frontalization using disentangled representation-learning cyclegan. Comput Vis and Image Underst 222:103526. https://doi.org/10.1016/j.cviu.2022.103526

    Article  Google Scholar 

  316. Makhmudkhujaev F, Hong S, Kyu Park I (2021) Re-aging gan: toward personalized face age transformation. In: Proceedings of the IEEE/CVF international conference on computer vision, Montréal, QC, Canada, pp 3888–3897. https://doi.org/10.1109/ICCV48922.2021.00388

  317. Li C, Li Y, Weng Z et al (2023) Face aging with feature-guide conditional generative adversarial network. Electronics 12(9):2095. https://doi.org/10.3390/electronics12092095

    Article  Google Scholar 

  318. Tang X, Wang Z, Luo W et al (2018) Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 7939–7947. https://doi.org/10.1109/CVPR.2018.00828

  319. Huang X, Gong M (2022) Landmark-guided conditional gans for face aging. In: Sclaroff S, Distante C, Leo M et al (eds) Image Analysis and Processing – ICIAP 2022. Springer International Publishing, Cham, pp 270–283. https://doi.org/10.1007/978-3-031-06427-2_23

  320. Fang H, Deng W, Zhong Y et al (2020) Triple-gan: progressive face aging with triple translation loss. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Seattle, WA, USA, pp 3500–3509, https://doi.org/10.1109/CVPRW50498.2020.00410

  321. Huang Z, Chen S, Zhang J et al (2021) Pfa-gan: Progressive face aging with generative adversarial network. IEEE Trans Inf Forensics Secur 16:2031–2045. https://doi.org/10.1109/TIFS.2020.3047753

    Article  Google Scholar 

  322. Li P, Hu Y, He R et al (2019) Global and local consistent wavelet-domain age synthesis. IEEE Trans Inf Forensics Secur 14(11):2943–2957. https://doi.org/10.1109/TIFS.2019.2907973

    Article  Google Scholar 

  323. Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 4352–4360. https://doi.org/10.1109/CVPR.2017.463

  324. Park T, Liu MY, Wang TC et al (2019) Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp 2337–2346, https://doi.org/10.1109/CVPR.2019.00244

  325. Zhu JY, Krähenbühl P, Shechtman E et al (2016) Generative visual manipulation on the natural image manifold. In: Leibe B, Matas J, Sebe N, et al (eds) Computer Vision – ECCV 2016. Springer International Publishing, Cham, pp 597–613, https://doi.org/10.1007/978-3-319-46454-1_36

  326. Lin J, Zhang R, Ganz F et al (2021) Anycost gans for interactive image synthesis and editing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Nashville, TN, USA, pp 14981–14991, https://doi.org/10.1109/CVPR46437.2021.01474

  327. Nam S, Kim Y, Kim SJ (2018) Text-adaptive generative adversarial networks: manipulating images with natural language. In: Proceedings of the 32nd international conference on neural information processing systems, Montréal, Canada, pp 42–51

  328. Lee CH, Liu Z, Wu L et al (2020) Maskgan: towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 5548–5557, https://doi.org/10.1109/CVPR42600.2020.00559

  329. Ling H, Kreis K, Li D et al (2021) Editgan: high-precision semantic image editing. In: Proceedings of the 35th international conference on neural information processing systems, [Virtual], pp 16331–16345

  330. Park T, Liu MY, Wang TC et al (2019) Gaugan: semantic image synthesis with spatially adaptive normalization. In: Proceedings of the special interest group on computer graphics and interactive techniques conference, Los Angeles, CA, USA, p 2. https://doi.org/10.1145/3306305.3332370

  331. Pan X, Tewari A, Leimkuhler T et al (2023) Drag your gan: interactive point-based manipulation on the generative image manifold. In: Proceedings of the special interest group on computer graphics and interactive techniques conference, Los Angeles, CA, USA, p 78, https://doi.org/10.1145/3588432.3591500

  332. Ehsani K, Mottaghi R, Farhadi A (2018) Segan: segmenting and generating the invisible. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 6144–6153. https://doi.org/10.1109/CVPR.2018.00643

  333. Li J, Liang X, Wei Y et al (2017) Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 1951–1959. https://doi.org/10.1109/CVPR.2017.211

  334. Bai Y, Zhang Y, Ding M et al (2018) Sod-mtgan: small object detection via multi-task generative adversarial network. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 210–226, https://doi.org/10.1007/978-3-030-01261-8_13

  335. Prakash CD, Karam LJ (2021) It gan do better: Gan-based detection of objects on images with varying quality. IEEE Trans Image Process 30:9220–9230. https://doi.org/10.1109/TIP.2021.3124155

    Article  Google Scholar 

  336. Zhang Z, Pei Z, Tang Z et al (2022) Odem-gan: an object deformation enhancement model based on generative adversarial networks. Appl Sci 12(9):4609. https://doi.org/10.3390/app12094609

    Article  Google Scholar 

  337. Luo Z, Ding S (2019) Object detection in remote sensing images based on gan. In: Proceedings of the international conference on artificial intelligence and computer science, Wuhan, China, pp 499–503, https://doi.org/10.1145/3349341.3349458

  338. Cheng S, Yao P, Deng K et al (2022) Detgan: Gan for arbitrary-oriented object detection in remote sensing images. In: Proceedings of the Asia conference on algorithms, computing and machine learning, Hangzhou, China, pp 337–341, https://doi.org/10.1109/CACML55074.2022.00063

  339. Chen X, Xu C, Yang X et al (2018) Attention-gan for object transfiguration in wild images. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 167–184, https://doi.org/10.1007/978-3-030-01216-8_11

  340. Wu H, Zheng S, Zhang J et al (2019) Gp-gan: towards realistic high-resolution image blending. https://doi.org/10.48550/arXiv.1703.07195. arXiv:1703.07195v3

  341. Chen BC, Kae A (2019) Toward realistic image compositing with adversarial learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp 8407–8416. https://doi.org/10.1109/CVPR.2019.00861

  342. Yang J, Kannan A, Batra D (2017) Lr-gan: layered recursive generative adversarial networks for image generation. In: Proceedings of the 5th international conference on learning representations, Toulon, France, pp 1–21, available at https://openreview.net/forum?id=HJ1kmv9xx

  343. Zhu W, Xiang X, Tran TD et al (2018) Adversarial deep structured nets for mass segmentation from mammograms. In: Proceedings of the IEEE 15th international symposium on biomedical imaging, Washington, DC, USA, pp 847–850. https://doi.org/10.1109/ISBI.2018.8363704

  344. Dong H, Yu S, Wu C et al (2017) Semantic image synthesis via adversarial learning. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 5707–5715. https://doi.org/10.1109/ICCV.2017.608

  345. Souly N, Spampinato C, Shah M (2017) Semi supervised semantic segmentation using generative adversarial network. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 5688–5696. https://doi.org/10.1109/ICCV.2017.606

  346. Li T, Qian R, Dong C et al (2018) Beautygan: instance- level facial makeup transfer with the deep generative adversarial network. In: Proceedings of the 26th ACM international conference on multimedia, Seoul, Republic of Korea, pp 645–653. https://doi.org/10.1145/3240508.3240618

  347. Xu Z, Wu S, Jiao Q et al (2022) Tsev-gan: generative adversarial networks with target-aware style encoding and verification for facial makeup transfer. Knowledge-Based Syst 257:109958. https://doi.org/10.1016/j.knosys.2022.109958

    Article  Google Scholar 

  348. Horita D, Aizawa K (2022) Slgan: Style- and latent-guided generative adversarial network for desirable makeup transfer and removal. In: Proceedings of the 4th ACM international conference on multimedia in Asia, Tokyo, Japan, pp 1–5. https://doi.org/10.1145/3551626.3564967

  349. Duan Q, Zhang L, Gao X (2022) Simultaneous face completion and frontalization via mask guided two-stage. IEEE Trans Circuits Syst Video Technol 32(6):3761–3773. https://doi.org/10.1109/TCSVT.2021.3111648

    Article  Google Scholar 

  350. Wang Q, Fan H, Zhu L et al (2019) Deeply supervised face completion with multi-context generative adversarial network. IEEE Signal Process Lett 26(3):400–404. https://doi.org/10.1109/LSP.2018.2890205

    Article  Google Scholar 

  351. Cai J, Han H, Shan S et al (2020) Fcsr-gan: joint face completion and super-resolution via multi-task learning. IEEE Trans Biom Behav Ident Sci 2(2):109–121. https://doi.org/10.1109/TBIOM.2019.2951063

    Article  Google Scholar 

  352. Liu W, Hou X, Duan J et al (2020) End-to-end single image fog removal using enhanced cycle consistent adversarial networks. IEEE Trans Image Process 29:7819–7833. https://doi.org/10.1109/TIP.2020.3007844

    Article  Google Scholar 

  353. Manu CM, Sreeni KG (2023) Ganid: a novel generative adversarial network for image dehazing. Vis Comput 39:3923–3936. https://doi.org/10.1007/s00371-022-02536-9

    Article  Google Scholar 

  354. Ma Y, Xu J, Jia F et al (2022) Single image dehazing using generative adversarial networks based on an attention mechanism. IET Image Process 16:1897–1907. https://doi.org/10.1049/ipr2.12455

    Article  Google Scholar 

  355. Lutz S, Amplianitis K, Smolic A (2018) Alphagan: generative adversarial networks for natural image matting. In: Proceedings of the British machine vision conference, Newcastle, UK, p 259

  356. Ren X, Liu Y, Song C (2021) A generative adversarial framework for optimizing image matting and harmonization simultaneously. In: Proceedings of the IEEE international conference on image processing, Anchorage, AK, USA, pp 1354–1358, https://doi.org/10.1109/ICIP42928.2021.9506642

  357. Yu J, Lin Z, Yang J et al (2018) Generative image inpainting with contextual attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 5505–5514, https://doi.org/10.1109/CVPR.2018.00577

  358. Dolhansky B, Ferrer CC (2018) Eye in-painting with exemplar generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 7902–7911. https://doi.org/10.1109/CVPR.2018.00824

  359. Yuan L, Ruan C, Hu H et al (2019) Image inpainting based on patch-gans. IEEE Access 7:46411–46421. https://doi.org/10.1109/ACCESS.2019.2909553

    Article  Google Scholar 

  360. Yang Z, Chen Y, Le Z et al (2021) Ganfuse: a novel multi-exposure image fusion method based on generative adversarial networks. Neural Comput Appl 33:6133–6145. https://doi.org/10.1007/s00521-020-05387-4

    Article  Google Scholar 

  361. Zhang H, Yuan J, Tian X et al (2021) Gan-fm: infrared and visible image fusion using gan with full-scale skip connection and dual markovian discriminators. IEEE Trans Comput Imaging 7:1134–1147. https://doi.org/10.1109/TCI.2021.3119954

    Article  MathSciNet  Google Scholar 

  362. Xi X, Jin X, Jiang Q et al (2023) Ema-gan: a generative adversarial network for infrared and visible image fusion with multiscale attention network and expectation maximization algorithm. Adv Intell Syst 2023:2300310. https://doi.org/10.1002/aisy.202300310

    Article  Google Scholar 

  363. Liu F, Jiao L, Tang X (2019) Task-oriented gan for polsar image classification and clustering. IEEE Trans Neural Netw Learn Syst 30(9):2707–2719. https://doi.org/10.1109/TNNLS.2018.2885799

    Article  Google Scholar 

  364. Ss Jang, Cj Kim, Sy Hwang et al (2023) L-gan: landmark-based generative adversarial network for efficient face de-identification. J Supercomput 79:7132–7159. https://doi.org/10.1007/s11227-022-04954-x

    Article  Google Scholar 

  365. Xian W, Sangkloy P, Agrawal V et al (2018) Texturegan: controlling deep image synthesis with texture patches. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp 8456–8465. https://doi.org/10.1109/CVPR.2018.00882

  366. Li B, Zhu Y, Wang Y et al (2022) Anigan: style-guided generative adversarial networks for unsupervised anime face generation. IEEE Trans Multimed 24(4077–4091). https://doi.org/10.1109/TMM.2021.3113786

  367. Xia W, Xue JH (2023) A survey on deep generative 3d-aware image synthesis. ACM Comput Surv 56(4):90. https://doi.org/10.1145/3626193

    Article  MathSciNet  Google Scholar 

  368. Pan X, Dai B, Liu Z et al (2021) Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans. In: Proceedings of the 9th international conference on learning representations, [Virtual], pp 1–18, available at https://openreview.net/forum?id=FGqiDsBUKL0

  369. Harkonen E, Hertzmann A, Lehtinen J et al (2020) Ganspace: discovering interpretable gan controls. In: Proceedings of the 34th international conference on neural information processing systems, [Virtual], pp 9841–9850

  370. Shen Y, Zhou B (2021) Closed-form factorization of latent semantics in gans. In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, Nashville, TN, USA, pp 1532–1540. https://doi.org/10.1109/CVPR46437.2021.00158

  371. Tewari A, Elgharib M, Bharaj G et al (2020) Stylerig: rigging stylegan for 3d control over portrait images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 6141–6150. https://doi.org/10.1109/CVPR42600.2020.00618

  372. Deng Y, Yang J, Chen D et al (2020) Disentangled and controllable face image generation via 3d imitative-contrastive learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, pp 5153–5162. https://doi.org/10.1109/CVPR42600.2020.00520

  373. Shi Z, Shen Y, Zhu J et al (2022) 3d-aware indoor scene synthesis with depth priors. In: Avidan S, Brostow G, Cissé M et al (eds) Computer Vision – ECCV 2022. Springer Nature Switzerland, Cham, pp 406–422.https://doi.org/10.1007/978-3-031-19787-1_23

  374. Shoshan A, Bhonker N, Kviatkovsky I et al (2021) Gan-control: explicitly controllable gans. In: Proceedings of the IEEE/CVF international conference on computer vision, Montreal, QC, Canada, pp 14063–14073, https://doi.org/10.1109/ICCV48922.2021.01382

  375. Shi Y, Aggarwal D, Jain AK (2021) Lifting 2d stylegan for 3d-aware face generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Nashville, TN, USA, pp 6254–6262. https://doi.org/10.1109/CVPR46437.2021.00619

  376. Nguyen-Phuoc T, Li C, Theis L et al (2019) Hologan: unsupervised learning of 3d representations from natural images. In: Proceedings of the IEEE/CVF international conference on computer vision workshop, Seoul, South Korea, pp 2037–2040. https://doi.org/10.1109/ICCVW.2019.00255

  377. Nguyen-Phuoc T, Richardt C, Mai L et al (2020) Blockgan: learning 3d object-aware scene representations from unlabelled images. In: Proceedings of the 34th international conference on neural information processing systems, [Virtual], pp 6767–6778

  378. Chan ER, Monteiro M, Kellnhofer P et al (2021) pi-gan: periodic implicit generative adversarial networks for 3d-aware image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Nashville, TN, USA, pp 5795–5805, https://doi.org/10.1109/CVPR46437.2021.00574

  379. Niemeyer M, Geiger A (2021) Giraffe: representing scenes as compositional generative neural feature fields. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Nashville, TN, USA, pp 11448–11459. https://doi.org/10.1109/CVPR46437.2021.01129

  380. Xue Y, Li Y, Singh KK et al (2022) Giraffe hd: a high-resolution 3d-aware generative model. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, New Orleans, LA, USA, pp 18419–18428. https://doi.org/10.1109/CVPR52688.2022.01789

  381. Bahmani S, Park JJ, Paschalidou D et al (2023) 3d-aware video generation. Transact Mach Learn Res

  382. Xu Z, Zhang J, Liew JH et al (2023) Pv3d: a 3d generative model for portrait video generation. In: Proceedings of the 11th international conference on learning representations, Kigali, Rwanda, pp 1–17, available at https://openreview.net/forum?id=o3yygm3lnzS

  383. Aldausari N, Sowmya A, Marcus N et al (2022) Video generative adversarial networks: a review. ACM Comput Surv 55(2):30. https://doi.org/10.1145/3487891

    Article  Google Scholar 

  384. Wang C, Xu C, Wang C et al (2018) Perceptual adversarial networks for image-to-image transformation. IEEE Trans Image Process 27(8):4066–4079. https://doi.org/10.1109/TIP.2018.2836316

    Article  MathSciNet  Google Scholar 

  385. Tang H, Liu H, Xu D et al (2023) Attentiongan: unpaired image-to-image translation using attention-guided generative adversarial networks. IEEE Trans Neural Netw Learn Syst 34(4):1972–1987. https://doi.org/10.1109/TNNLS.2021.3105725

    Article  Google Scholar 

  386. Li Y, Tang S, Zhang R et al (2019) Asymmetric gan for unpaired image-to-image translation. IEEE Trans Image Process 28(12):5881–5896. https://doi.org/10.1109/TIP.2019.2922854

    Article  MathSciNet  Google Scholar 

  387. Ko K, Yeom T, Lee M (2023) Superstargan: generative adversarial networks for image-to-image translation in large-scale domains. Neural Netw 162:330–339. https://doi.org/10.1016/j.neunet.2023.02.042

    Article  Google Scholar 

  388. Lee HY, Tseng HY, Huang JB et al (2018) Diverse image-to-image translation via disentangled representations. In: Ferrari V, Hebert M, Sminchisescu C et al (eds) Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp 36–52. https://doi.org/10.1007/978-3-030-01246-5_3

  389. Lee HY, Tseng HY, Mao Q et al (2020) Drit++: diverse image-to-image translation via disentangled representations. Int J Comput Vis 128:2402–2417. https://doi.org/10.1007/s11263-019-01284-z

    Article  Google Scholar 

  390. Zhang H, Xu T, Li H et al (2017) Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 5908–5916, https://doi.org/10.1109/ICCV.2017.629

  391. Dash A, Gamboa J, Ahmed S et al (2017) Tac-gan - text conditioned auxiliary classifier generative adversarial network. https://doi.org/10.48550/arXiv.1703.06412. arXiv:1703.06412v2

  392. Tan YX, Lee CP, Neo M et al (2022) Text-to-image synthesis with self-supervised learning. Pattern Recognit Lett 157:119–126. https://doi.org/10.1016/j.patrec.2022.04.010

    Article  Google Scholar 

  393. Dong Y, Zhang Y, Ma L et al (2021) Unsupervised text-to-image synthesis. Pattern Recognit Lett 110:107573. https://doi.org/10.1016/j.patcog.2020.107573

    Article  Google Scholar 

  394. Tao M, Tang H, Wu F et al (2022) Df-gan: a simple and effective baseline for text-to-image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, New Orleans, LA, USA, pp 16494–16504. https://doi.org/10.1109/CVPR52688.2022.01602

  395. Jiang B, Zeng W, Yang C et al (2023) De-gan: text-to-image synthesis with dual and efficient fusion model. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16377-8

    Article  Google Scholar 

  396. Jeon E, Kim K, Kim D (2021) Fa-gan: feature-aware gan for text to image synthesis. In: Proceedings of the IEEE international conference on image processing, Anchorage, AK, USA, pp 2443–2447. https://doi.org/10.1109/ICIP42928.2021.9506172

  397. Kang M, Zhu JY, Zhang R et al (2023) Scaling up gans for text-to-image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Vancouver, BC, Canada, pp 10124–10134. https://doi.org/10.1109/CVPR52729.2023.00976

  398. Brophy E, Wang Z, She Q et al (2023) Generative adversarial networks in time series: a systematic literature review. ACM Comput Surv 55(10):199. https://doi.org/10.1145/3559540

    Article  Google Scholar 

  399. Zhang D, Ma M, Xia L (2022) A comprehensive review on gans for time-series signals. Neural Comput Appl 34:3551–571. https://doi.org/10.1007/s00521-022-06888-0

    Article  Google Scholar 

  400. Yu L, Zhang W, Wang J et al (2017) Seqgan: sequence generative adversarial nets with policy gradient. In: Proceedings of the 31st AAAI conference on artificial intelligence, San Francisco, CA, USA, pp 2852–2858

  401. Li J, Monroe W, Shi T et al (2017) Adversarial learning for neural dialogue generation. In: Proceedings of the 2017 conference on empirical methods in natural language processing, Copenhagen, Denmark, pp 2157–2169. https://doi.org/10.18653/v1/D17-1230

  402. Su H, Shen X, Hu P et al (2018) Dialogue generation with gan. In: Proceedings of the 32nd AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, pp 8163–8164. https://doi.org/10.1609/aaai.v32i1.12158

  403. Zhang Y, Gan Z, Fan K et al (2017) Adversarial feature matching for text generation. In: Proceedings of the 34th international conference on machine learning, Sydney, Australia, pp 4006–4015

  404. Lin K, Li D, He X et al (2017) Adversarial ranking for language generation. In: Proceedings of the 31st international conference on neural information processing systems, Long Beach, CA, USA, pp 3158–3168

  405. Nie W, Narodytska N, Patel A (2019) Relgan: relational generative adversarial networks for text generation. In: Proceedings of the 7th international conference on learning representations, New Orleans, Louisiana, USA, pp 1–20, available at https://openreview.net/forum?id=rJedV3R5tm

  406. Xu J, Ren X, Lin J et al (2018) Diversity-promoting gan: a cross-entropy based generative adversarial network for diversified text generation. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, pp 3940–3949. https://doi.org/10.18653/v1/D18-1428

  407. d’Autume CdM, Rosca M, Rae J et al (2019) Training language gans from scratch. In: Proceedings of the 33rd international conference on neural information processing systems, Vancouver, Canada, pp 4300–4311

  408. Wang J, Yu L, Zhang W et al (2017) Irgan: A minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, Tokyo, Japan, pp 515–524. https://doi.org/10.1145/3077136.3080786

  409. Liu B, Fu J, Kato MP et al (2018) Beyond narrative description: generating poetry from images by multi-adversarial training. In: Proceedings of the 26th ACM international conference on multimedia, Seoul, South Korea, pp 783–791. https://doi.org/10.1145/3240508.3240587

  410. Cai L, Wang WY (2018) Kbgan: adversarial learning for knowledge graph embeddings. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, New Orleans, Louisiana, USA, pp 1470–1480. https://doi.org/10.18653/v1/N18-1133

  411. Yang X, Khabsa M, Wang M et al (2019) Adversarial training for community question answer selection based on multi-scale matching. In: Proceedings of the proceedings of the aaai conference on artificial intelligence, Honolulu, Hawaii, USA, pp 395–402. https://doi.org/10.1609/aaai.v33i01.3301395

  412. Wang X, Chen W, Wang YF et al (2018) No metrics are perfect: Adversarial reward learning for visual storytelling. In: Proceedings of the 56th annual meeting of the association for computational linguistics, Melbourne, Australia, pp 899–909. https://doi.org/10.18653/v1/P18-1083

  413. Chen TH, Liao YH, Chuang CY et al (2017) Show, adapt and tell: adversarial training of cross-domain image captioner. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp 521–530, https://doi.org/10.1109/ICCV.2017.64

  414. Luo Y, Zhang H, Wen Y et al (2019) Resumegan: an optimized deep representation learning framework for talent-job fit via adversarial learning. In: Proceedings of the 28th ACM international conference on information and knowledge management, Beijing, China, pp 1101–1110. https://doi.org/10.1145/3357384.3357899

  415. Donahue C, Li B, Prabhavalkar R (2018) Exploring speech enhancement with generative adversarial networks for robust speech recognition. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Calgary, AB, Canada, pp 5024–5028. https://doi.org/10.1109/ICASSP.2018.8462581

  416. Hsu CC, Hwang HT, Wu YC et al (2017) Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. In: Proceedings of the interspeech, Stockholm, Sweden, pp 3364–3368. https://doi.org/10.21437/Interspeech.2017-63

  417. Pascual S, Bonafonte A, Serra J (2017) Segan: speech enhancement generative adversarial network. In: Proceedings of the interspeech, Stockholm, Sweden, pp 3624–3646. https://doi.org/10.21437/Interspeech.2017-1428

  418. Phan H, Nguyen HL, Chén OY et al (2021) Self-attention generative adversarial network for speech enhancement. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Toronto, ON, Canada, pp 7103–7107. https://doi.org/10.1109/ICASSP39728.2021.9414265

  419. Yang F, Wang Z, Li J et al (2020) Improving generative adversarial networks for speech enhancement through regularization of latent representations. Speech Commun 118:1–9. https://doi.org/10.1016/j.specom.2020.02.001

    Article  Google Scholar 

  420. Saito Y, Takamichi S, Saruwatari H (2018) Statistical parametric speech synthesis incorporating generative adversarial networks. IEEE-ACM Trans Audio Speech Lang 26(1):84–96. https://doi.org/10.1109/TASLP.2017.2761547

    Article  Google Scholar 

  421. Mogren O (2016) C-rnn-gan: continuous recurrent neural networks with adversarial training. In: Proceedings of the 30th NIPS workshop on constructive machine learning, Barcelona, Spain, pp 1–6

  422. Guimaraes GL, Sanchez-Lengeling B, Outeiral C et al (2018) Objective-reinforced generative adversarial networks (organ) for sequence generation models. https://doi.org/10.48550/arXiv.1705.10843. arXiv:1705.10843v3

  423. Lee Sg, Hwang U, Min S et al (2018) Polyphonic music generation with sequence generative adversarial networks. https://doi.org/10.48550/arXiv.1710.11418. arXiv:1710.11418v2

  424. Dong HW, Hsiao WY, Yang LC et al (2018) Musegan: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Proceedings of the 32nd AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, pp 34–41. https://doi.org/10.1609/aaai.v32i1.11312

  425. Huang W, Xue Y, Xu Z et al (2022) Polyphonic music generation generative adversarial network with markov decision process. Multimed Tools Appl 81:29865–29885. https://doi.org/10.1007/s11042-022-12925-w

    Article  Google Scholar 

  426. Yoon J, Jarrett D, van der Schaar M (2019) Time-series generative adversarial networks. In: Proceedings of the 33rd international conference on neural information processing systems, Vancouver, Canada, pp 5508–5518

  427. Tschuchnig ME, Ferner C, Wegenkittl S (2020) Sequential iot data augmentation using generative adversarial networks. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Barcelona, Spain, pp 4212–4216. https://doi.org/10.1109/ICASSP40776.2020.9053949

  428. Weng P, Tian Y, Liu Y et al (2023) Time-series generative adversarial networks for flood forecasting. J Hydrol 622:129702. https://doi.org/10.1016/j.jhydrol.2023.129702

    Article  Google Scholar 

  429. Ni H, Szpruch L, Sabate-Vidales M et al (2021) Sig-wasserstein gans for time series generation. In: Proceedings of the 2nd ACM international conference on ai in finance, [Virtual], pp 1–8. https://doi.org/10.1145/3490354.3494393

  430. Wiese M, Knobloch R, Korn R et al (2020) Quant gans: deep generation of financial time series. Quant Financ 20(9):1419–1440. https://doi.org/10.1080/14697688.2020.1730426

    Article  MathSciNet  Google Scholar 

  431. Takahashi S, Chen Y, Tanaka-Ishii K (2019) Modeling financial time-series with generative adversarial networks. Physica A 527:121261. https://doi.org/10.1016/j.physa.2019.121261

    Article  Google Scholar 

  432. Li X, Jiang Y, Rodriguez-Andina JJ et al (2021) When medical images meet generative adversarial network: recent development and research opportunities. Discov Artif Intell 1:5. https://doi.org/10.1007/s44163-021-00006-0

    Article  Google Scholar 

  433. Alamir M, Alghamdi M (2023) The role of generative adversarial network in medical image analysis: an in-depth survey. ACM Comput Surv 55(5):96. https://doi.org/10.1145/3527849

    Article  Google Scholar 

  434. Chen Y, Yang XH, Wei Z et al (2022) Generative adversarial networks in medical image augmentation: a review. Comput Biol Med 144:105382. https://doi.org/10.1016/j.compbiomed.2022.105382

    Article  Google Scholar 

  435. Xun S, Li D, Zhu H et al (2022) Generative adversarial networks in medical image segmentation: a review. Comput Biol Med 140:105063. https://doi.org/10.1016/j.compbiomed.2021.105063

    Article  Google Scholar 

  436. Jeong JJ, Tariq A, Adejumo T et al (2022) Systematic review of generative adversarial networks (gans) for medical image classification and segmentation. J Digit Imaging 35(2):137–152. https://doi.org/10.1007/s10278-021-00556-w

    Article  Google Scholar 

  437. Islam J, Zhang Y (2020) Gan-based synthetic brain pet image generation. Brain Inf 7:3. https://doi.org/10.1186/s40708-020-00104-2

    Article  Google Scholar 

  438. Lan H, Toga AW, Sepehrband F (2021) Three-dimensional self-attention conditional gan with spectral normalization for multimodal neuroimaging synthesis. Magn Reson Med 86(3):1718–1733. https://doi.org/10.1002/mrm.28819

    Article  Google Scholar 

  439. Oulbacha R, Kadoury S (2020) Mri to ct synthesis of the lumbar spine from a pseudo-3d cycle gan. In: Proceedings of the 17th IEEE international symposium on biomedical imaging, Iowa City, IA, USA, pp 1784–1787. https://doi.org/10.1109/ISBI45749.2020.9098421

  440. Kazeminia S, Baur C, Kuijper A et al (2020) Gans for medical image analysis. Artif Intell Med 109:101938. https://doi.org/10.1016/j.artmed.2020.101938

    Article  Google Scholar 

  441. Zhang R, Lu W, Wei X et al (2022) A progressive generative adversarial method for structurally inadequate medical image data augmentation. IEEE J Biomed Health Inform 26(1):7–16. https://doi.org/10.1109/JBHI.2021.3101551

    Article  Google Scholar 

  442. Choi E, Biswal S, Malin B et al (2017) Generating multi-label discrete patient records using generative adversarial networks. In: Proceedings of the 2nd machine learning for healthcare conference, Boston, Massachusetts, USA, pp 286–305

  443. Armanious K, Yang C, Fischer M et al (2020) Medgan: medical image translation using gans. Comput Med Imaging Graph 79:101684. https://doi.org/10.1016/j.compmedimag.2019.101684

    Article  Google Scholar 

  444. Albahli S (2020) Efficient gan-based chest radiographs (cxr) augmentation to diagnose coronavirus disease pneumonia. Int J Med Sci 17(10):1439–1448. https://doi.org/10.7150/ijms.46684

    Article  Google Scholar 

  445. Schlegl T, Seeböck P, Waldstein SM et al (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer M, Styner M, Aylward S et al (eds.) Information Processing in Medical Imaging. Springer International Publishing, Cham, pp 146–157. https://doi.org/10.1007/978-3-319-59050-9_12

  446. Murad T, Ali S, Patterson M (2023) Exploring the potential of gans in biological sequence analysis. Biology 12(6):854. https://doi.org/10.3390/biology12060854

    Article  Google Scholar 

  447. Hwang JJ, Azernikov S, Efros AA et al (2018) Learning beyond human expertise with generative models for dental restorations. https://doi.org/10.48550/arXiv.1804.00064. arXiv:1804.00064v1

  448. Ravindra Padalkar G, Dinkar Patil S, Mallikarjun Hegadi M et al (2021) Drug discovery using generative adversarial network with reinforcement learning. In: Proceedings of the international conference on computer communication and informatics, Coimbatore, India, pp 1–3. https://doi.org/10.1109/ICCCI50826.2021.9402449

  449. Fu Z, Wang F, Cheng X (2020) The secure steganography for hiding images via gan. EURASIP J Image Video Proc 2020:46. https://doi.org/10.1186/s13640-020-00534-2

    Article  Google Scholar 

  450. Shi H, Dong J, Wang W (2018) Ssgan: secure steganography based on generative adversarial networks. https://doi.org/10.48550/arXiv.1707.01613. arXiv:1707.01613v4

  451. Chen Z, Peng L, Hu A et al (2021) Generative adversarial network-based rogue device identification using differential constellation trace figure. EURASIP J Wireless Com Network 2021:72. https://doi.org/10.1186/s13638-021-01950-2

    Article  Google Scholar 

  452. Zhou F, Yang S, Fujita H et al (2020) Deep learning fault diagnosis method based on global optimization gan for unbalanced data. Knowl-Based Syst 187:104837. https://doi.org/10.1016/j.knosys.2019.07.008

    Article  Google Scholar 

  453. Wang J, Li S, Han B et al (2019) Generalization of deep neural networks for imbalanced fault classification of machinery using generative adversarial networks. IEEE Access 7:111168–111180. https://doi.org/10.1109/ACCESS.2019.2924003

    Article  Google Scholar 

  454. Wang J, Yang Z, Zhang J et al (2019) Adabalgan: an improved generative adversarial network with imbalanced learning for wafer defective pattern recognition. IEEE Trans Semicond Manuf 32(3):310–319. https://doi.org/10.1109/TSM.2019.2925361

    Article  Google Scholar 

  455. Bagheri A, Gu IYH, Bollen MHJ (2019) Generative adversarial model-guided deep active learning for voltage dip labelling. In: Proceedings of the IEEE Milan PowerTech, Milan, Italy, pp 1–5. https://doi.org/10.1109/PTC.2019.8810499

  456. Xu D, Wei C, Peng P et al (2020) Ge-gan: a novel deep learning framework for road traffic state estimation. Transp Res Pt C-Emerg Technol 117:102635. https://doi.org/10.1016/j.trc.2020.102635

    Article  Google Scholar 

  457. Sun AY (2018) Discovering state-parameter mappings in subsurface models using generative adversarial networks. Geophys Res Lett 45(20):11137–11146. https://doi.org/10.1029/2018GL080404

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University (No.2023D15), and Ningbo Clinical Research Center for Medical Imaging (No.2022LYKFYB01).

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  1. School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, 315211, People’s Republic of China

    Zeeshan Ahmad, Meng Chen & Shudi Bao

  2. School of International Education, Xiamen University of Technology, Xiamen, 361024, People’s Republic of China

    Zain ul Abidin Jaffri

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  1. Zeeshan Ahmad
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Ahmad, Z., Jaffri, Z.u.A., Chen, M. et al. Understanding GANs: fundamentals, variants, training challenges, applications, and open problems. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19361-y

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