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Beyond Supervised Learning: A Computer Vision Perspective

  • Review Article
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Journal of the Indian Institute of Science Aims and scope

Abstract

Fully supervised deep learning-based methods have created a profound impact in various fields of computer science. Compared to classical methods, supervised deep learning-based techniques face scalability issues as they require huge amounts of labeled data and, more significantly, are unable to generalize to multiple domains and tasks. In recent years, a lot of research has been targeted towards addressing these issues within the deep learning community. Although there have been extensive surveys on learning paradigms such as semi-supervised and unsupervised learning, there are a few timely reviews after the emergence of deep learning. In this paper, we provide an overview of the contemporary literature surrounding alternatives to fully supervised learning in the deep learning context. First, we summarize the relevant techniques that fall between the paradigm of supervised and unsupervised learning. Second, we take autonomous navigation as a running example to explain and compare different models. Finally, we highlight some shortcomings of current methods and suggest future directions.

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References

  1. Abadi M, Andersen DG (2016) Learning to protect communications with adversarial neural cryptography. CoRR. arXiv:1610.06918

  2. Abu-El-Haija S, Kothari N, Lee J, Natsev AP, Toderici G, Varadarajan B, Vijayanarasimhan S (2016) Youtube-8m: a large-scale video classification benchmark. arXiv:1609.08675v1

  3. Agrawal P, Carreira J, Malik J (2015) Learning to see by moving. In: International conference on computer vision (CVPR), Boston, MA, USA

  4. Akata Z, Perronnin F, Harchaoui Z, Schmid C (2013) Label-embedding for attribute-based classification. In: Computer vision and pattern recognition (CVPR), Portland, OR, USA

  5. Alhaija H, Mustikovela S, Mescheder L, Geiger A, Rother C (2018) Augmented reality meets computer vision: efficient data generation for urban driving scenes. Int J Comput Vis 126(9):961–972

    Article  Google Scholar 

  6. Andrychowicz M, Denil M, Gomez S, Hoffman MW, Pfau D, Schaul T, Shillingford B, De Freitas N (2016) Learning to learn by gradient descent by gradient descent. In: Advances in neural information processing systems (NIPS), Barcelona, Spain

  7. Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. CoRR. arXiv:1711.04340

  8. Arandjelovic R, Zisserman A (2017) Look, listen and learn. In: International conference on computervision (ICCV), Venice, Italy

  9. Arpit D, Jastrzębskis S, Ballas N, Krueger D, Bengio E, Kanwal MS, Maharaj T, Fischer A, Courville A, Bengio Y, et al (2017) A closer look at memorization in deep networks. In: International conference on machine learning (ICML), Sydney, Australia

  10. Aubry M, Russell BC (2015) Understanding deep features with computer-generated imagery. In:International conference on computer vision (ICCV), Santiago, Chile

  11. Aubry M, Maturana D, Efros AA, Russell BC, Sivic J (2014) Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of cad models. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  12. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  13. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: International conference on learning representations (ICLR), San Diego, CA, USA

  14. Bansal A, Sikka K, Sharma G, Chellappa R, Divakaran A (2018) Zero-shot object detection. In: European conference on computer vision (ECCV), Munich, Germany

  15. Bearman A, Russakovsky O, Ferrari V, Fei-Fei L (2016) Whats the point: Semantic segmentation with pointsupervision. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  16. Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Mach Learn 79(1–2):151–175

    Article  Google Scholar 

  17. Bilen H, Vedaldi A (2016) Weakly supervised deep detection networks. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  18. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Computational learning theory (CoLT), Madison, Wisconsin, USA

  19. Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D (2016) Domain separation networks. In: Advances in neural information processing systems (NIPS), Barcelona, Spain

  20. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  21. Busto PP, Gall J (2017) Open set domain adaptation. In: International conference on computer vision (ICCV), Venice, Italy

  22. Butler DJ, Wulff J, Stanley GB, Black MJ (2012) A naturalistic open source movie for optical flow evaluation. In: European conference on computer vision (ECCV), Firenze, Italy

  23. Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  24. Chapelle O, Scholkopf B, Zien A (2009) Semi-supervised learning (Chapelle O. et al., eds.; 2006) [book reviews]. IEEE Trans Neural Netw 20(3):542

    Article  Google Scholar 

  25. Chattopadhyay R, Sun Q, Fan W, Davidson I, Panchanathan S, Ye J (2012) Multi-source domain adaptation and its application to early detection of fatigue. Trans Knowl Discov Data (TKDD) 6(4):18

    Google Scholar 

  26. Chen C, Seff A, Kornhauser A, Xiao J (2015) Deepdriving: learning affordance for direct perception in autonomous driving. In: International conference on computer vision (ICCV), Santiago, Chile

  27. Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. CoRR. arXiv:1706.05587

  28. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  29. Chen TH, Liao YH, Chuang CY, Hsu WT, Fu J, Sun M (2017) Show, adapt and tell: adversarial training of cross-domain image captioner. In: International conference on computer vision (ICCV), Venice, Italy

  30. Chen X, Gupta A (2015) Webly supervised learning of convolutional networks. In: International conference on computer vision (ICCV), Santiago, Chile

  31. Chen Y, Li W, Sakaridis C, Dai D, Van Gool L (2018) Domain adaptive faster R-CNN for object detection in the wild. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  32. Chen YH, Chen WY, Chen YT, Tsai BC, Wang YCF, Sun M (2017) No more discrimination: cross city adaptation of road scene segmenters. In: International conference on computer vision (ICCV), Venice, Italy

  33. Chen Z, Liu B (2016) Lifelong machine learning. Synth Lect Artif Intell Mach Learn 10(3):1–145

    Article  CAS  Google Scholar 

  34. Cohn DA, Ghahramani Z, Jordan MI (1996) Active learning with statistical models. J Artif Intell Res 4:129–145

    Article  Google Scholar 

  35. Cordts M, Omran M, Ramos S, Scharwächter T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2015) The cityscapes dataset. In: CVPR workshop on the future of datasets in vision (CVPRW), Boston, MA, USA

  36. Courty N, Flamary R, Habrard A, Rakotomamonjy A (2017) Joint distribution optimal transportation for domain adaptation. In: Advances in neural information processing systems (NIPS), Long Beach, CA, USA

  37. Csurka G (2017) Domain adaptation for visual applications: a comprehensive survey. CoRR. arXiv:1702.05374

  38. Damodaran BB, Kellenberger B, Flamary R, Tuia D, Courty N (2018) Deepjdot: deep joint distribution optimal transport for unsupervised domain adaptation. In: European conference on computer vision (ECCV), Munich, Germany

  39. Daumé III H (2007) Frustratingly easy domain adaptation. In: Association of computational linguistics (ACL), Prague, Czech Republic

  40. Day O, Khoshgoftaar TM (2017) A survey on heterogeneous transfer learning. J Big Data 4(1):29

    Article  Google Scholar 

  41. De Souza CR, Gaidon A, Cabon Y, Peña AML (2017) Procedural generation of videos to train deep action recognition networks. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  42. Deng W, Zheng L, Kang G, Yang Y, Ye Q, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person reidentification. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  43. Divvala SK, Farhadi A, Guestrin C (2014) Learning everything about anything: webly-supervised visual concept learning. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  44. Doersch C, Gupta A, Efros AA (2015) Unsupervised visual representation learning by context prediction. In: International conference on computer vision (ICCV), Santiago, Chile

  45. Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) DeCAF: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning (ICML), Beijing, China

  46. Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  47. Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, Van Der Smagt P, Cremers D, Brox T (2015) FlowNet: learning optical flow with convolutional networks. In: International conference on computer vision (ICCV), Santiago, Chile

  48. Dosovitskiy A, Ros G, Codevilla F, Lopez A, Koltun V (2017) CARLA: an open urban driving simulator. In: Conference on robot learning (CoRL), Mountain View, California, USA

  49. Duan L, Xu D, Tsang I (2011) Learning with augmented features for heterogeneous domain adaptation. In: International conference on machine learning (ICML), Edinburgh, Scotland

  50. Duan L, Tsang IW, Xu D (2012) Domain transfer multiple kernel learning. Trans Pattern Anal Mach Intell 34(3):465–479

    Article  Google Scholar 

  51. Duchenne O, Audibert JY, Keriven R, Ponce J, Ségonne F (2008) Segmentation by transduction. In: Computer vision and pattern recognition (CVPR), Anchorage, AL, USA

  52. Dwibedi D, Misra I, Hebert M (2017) Cut, paste and learn: surprisingly easy synthesis for instance detection. In: International conference on computer vision (ICCV), Venice, Italy

  53. Elhamifar E, Sapiro G, Yang A, Shankar Sasrty S (2013) A convex optimization framework for active learning. In: International conference on computer vision (ICCV), Sydney, Australia

  54. Fan J, Shen Y, Zhou N, Gao Y (2010) Harvesting large-scale weakly-tagged image databases from the web. In: Computer vision and pattern recognition (CVPR), San Francisco, CA, USA

  55. Fang M, Li Y, Cohn T (2017) Learning how to active learn: a deep reinforcement learning approach. In: Association of computational linguistics (ACL), Vancouver, Canada

  56. Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: Computer vision and pattern recognition (CVPR), Miami, FL, USA

  57. Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. Trans Pattern Anal Mach Intell 28(4):594–611

    Article  Google Scholar 

  58. Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  59. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference of machine learning (ICML), Sydney, Australia

  60. Frénay B, Verleysen M (2014) Classification in the presence of label noise: a survey. Trans Neural Netw Learn Syst 25(5):845–869

    Article  Google Scholar 

  61. Freytag A, Rodner E, Denzler J (2014) Selecting influential examples: active learning with expected model output changes. In: European conference on computer vision (ECCV), Zurich, Switzerland

  62. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. CoRR. arXiv:1803.01229

  63. Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Mikolov T et al (2013) Devise: a deep visual-semantic embedding model. In: Advances in neural information processing systems (NIPS), Stateline, NA, USA

  64. Gaidon A, Wang Q, Cabon Y, Vig E (2016) Virtual worlds as proxy for multi-object tracking analysis. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  65. Gal Y, Islam R, Ghahramani Z (2017) Deep Bayesian active learning with image data. In: Advances in neural information processing systems workshops, Long Beach, CA, USA

  66. Gan C, Sun C, Duan L, Gong B (2016) Webly-supervised video recognition by mutually voting for relevant web images and web video frames. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  67. Gan C, Yao T, Yang K, Yang Y, Mei T (2016) You lead, we exceed: labor-free video concept learning by jointly exploiting web videos and images. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  68. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    Google Scholar 

  69. Gao M, Li A, Yu R, Morariu VI, Davis LS (2018) C-WSL: count-guided weakly supervised localization. In: Europeanconference on computer vision (ECCV), Munich, Germany

  70. Gebru T, Hoffman J, Fei-Fei L (2017) Fine-grained recognition in the wild: a multi-task domain adaptation approach. In: International conference on computer vision (ICCV), Venice, Italy

  71. Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the KITTI dataset. Int J Robot Res 32(11):1231–1237

    Article  Google Scholar 

  72. Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W (2016) Deep reconstruction-classification networks for unsupervised domain adaptation. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  73. Ghosh A, Kumar H, Sastry P (2017) Robust loss functions under label noise for deep neural networks. In: AAAI, San Francisco, CA, USA

  74. Girdhar R, Ramanan D, Gupta A, Sivic J, Russell B (2017) ActionVLAD: learning spatio-temporal aggregation for action classification. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  75. Girshick R (2015) Fast R-CNN. In: International conference on computer vision (ICCV), Santiago, Chile

  76. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  77. Gomez L, Patel Y, Rusiñol M, Karatzas D, Jawahar C (2017) Self-supervised learning of visual features through embedding images into text topic spaces. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  78. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Computer vision and pattern recognition (CVPR), Providence, RI, USA

  79. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems (NIPS), Montreal, Canada

  80. Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: an unsupervised approach. In: International conference on computer vision (ICCV), Barcelona, Spain

  81. Goyal Y, Khot T, Summers-Stay D, Batra D, Parikh D (2017) Making the V in VQA matter: elevating the role of image understanding in Visual Question Answering. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  82. Graves A (2013) Generating sequences with recurrent neural networks. CoRR. arXiv:1308.0850

  83. Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning (ICML), Beijing, China

  84. Gu J, Neubig G, Cho K, Li VO (2017) Learning to translate in real-time with neural machine translation. In: Association of computational linguistics (ACL), Vancouver, Canada

  85. Gupta A, Vedaldi A, Zisserman A (2016) Synthetic data for text localisation in natural images. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  86. Habibian A, Mensink T, Snoek CG (2014) Composite concept discovery for zero-shot video event detection. In: International conference on multimedia retrieval (ICMR), Glasgow, UK

  87. Haeusser P, Frerix T, Mordvintsev A, Cremers D (2017) Associative domain adaptation. In: International conference on computer vision (ICCV), Venice, Italy

  88. Handa A, Whelan T, McDonald J, Davison AJ (2014) A benchmark for RGB-D visual odometry, 3D reconstruction and slam. In: International conference on robotics and automation (ICRA), Hong Kong

  89. Handa A, Patraucean V, Badrinarayanan V, Stent S, Cipolla R (2016) Understanding real world indoor scenes with synthetic data. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  90. Hariharan B, Girshick RB (2017) Low-shot visual recognition by shrinking and hallucinating features. In: International conference on computer vision (ICCV), Venice, Italy

  91. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  92. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: International conference on computer vision (ICCV), Honolulu, HI, USA

  93. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  CAS  Google Scholar 

  94. Hoffman J, Gupta S, Leong J, Guadarrama S, Darrell T (2016) Cross-modal adaptation for RGB-D detection. In: International conference on robotics and automation (ICRA), Stockholm, Sweden

  95. Hoffman J, Wang D, Yu F, Darrell T (2016) FCNs in the wild: pixel-level adversarial and constraint-based adaptation. CoRR. arXiv:1612.02649

  96. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  97. Huang J, Gretton A, Borgwardt KM, Schölkopf B, Smola AJ (2007) Correcting sample selection bias by unlabeled data. In: Advances in neural information processing systems (NIPS), Vancouver, Canada

  98. Huang Z, Wang X, Wang J, Liu W, Wang J (2018) Weakly-supervised semantic segmentation network with deep seeded region growing. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  99. Huh M, Liu A, Owens A, Efros AA (2018) Fighting fake news: image splice detection via learned self-consistency. In: European conference on computer vision (ECCV), Munich, Germany

  100. Ilse M, Tomczak JM, Welling M (2018) Attention-based deep multiple instance learning. In: International conference on machine learning (ICML), New Orleans, LA, USA

  101. Inoue N, Furuta R, Yamasaki T, Aizawa K (2018) Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  102. Janai J, Güney F, Behl A, Geiger A (2017) Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. CoRR. arXiv:1704.05519

  103. Jayaraman D, Grauman K (2015) Learning image representations tied to ego-motion. In: International conference on computer vision (CVPR), Boston, MA, USA

  104. Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. Trans Pattern Anal Mach Intell 35(1):221–231

    Article  Google Scholar 

  105. Jiang H, Larsson G, Maire M, Shakhnarovich G, Learned-Miller E (2018) Self-supervised relative depth learning for urban scene understanding. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  106. Johnson M, Schuster M, Le QV, Krikun M, Wu Y, Chen Z, Thorat N, Viégas F, Wattenberg M, Corrado G et al (2017) Google’s multilingual neural machine translation system: enabling zero-shot translation. In: Association of computational linguistics (ACL), Vancouver, Canada

  107. Joulin A, van der Maaten L, Jabri A, Vasilache N (2016) Learning visual features from large weakly supervised data. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  108. Kaneva B, Torralba A, Freeman WT (2011) Evaluation of image features using a photorealistic virtual world. In: International conference on computer vision (ICCV), Barcelona, Spain

  109. Kapoor A, Hua G, Akbarzadeh A, Baker S (2009) Which faces to tag: adding prior constraints into active learning. In: International conference on computer vision (ICCV), Kyoto, Japan

  110. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  111. Khoreva A, Benenson R, Hosang JH, Hein M, Schiele B (2017) Simple does it: weakly supervised instance and semantic segmentation. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  112. Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: International conference on machine learning (ICML), Sydney, Australia

  113. Kingma DP, Welling M (2013) Auto-encoding variational Bayes. In: International conference on learning representations (ICLR), Scottsdale, AZ, USA

  114. Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, Lille, France

  115. Krishna R, Zhu Y, Groth O, Johnson J, Hata K, Kravitz J, Chen S, Kalantidis Y, Li LJ, Shamma DA et al (2017) Visual genome: connecting language and vision using crowdsourced dense image annotations. Int J Comput Vis 123(1):32–73

    Article  Google Scholar 

  116. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), Stateline, NV, USA

  117. Kulis B, Saenko K, Darrell T (2011) What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Computer vision and pattern recognition (CVPR), Colorado Springs, CO, USA

  118. Kumar MP, Packer B, Koller D (2010) Self-paced learning for latent variable models. In: Advances in neural information processing systems (NIPS), Vancouver, Canada

  119. Kurakin A, Goodfellow I, Bengio S (2015) Adversarial examples in the physical world. In: International conference on learning representations (ICLR), San Diego, CA, USA

  120. Kuznetsova A, Rom H, Alldrin N, Uijlings J, Krasin I, Pont-Tuset J, Kamali S, Popov S, Malloci M, Duerig T, Ferrari V (2018) The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. CoRR. arXiv:1811.00982

  121. Lake BM, Salakhutdinov RR, Tenenbaum J (2013) One-shot learning by inverting a compositional causal process. In: Advances in neural information processing systems (NIPS), Stateline, NA, USA

  122. Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–1338

    Article  CAS  Google Scholar 

  123. Lampert CH, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. In: Computer vision and pattern recognition, 2009 (CVPR), Miami, FL, USA

  124. Lampert CH, Nickisch H, Harmeling S (2014) Attribute-based classification for zero-shot visual object categorization. Trans Pattern Anal Mach Intell 36(3):453–465

    Article  Google Scholar 

  125. Larsson G, Maire M, Shakhnarovich G (2017) Colorization as a proxy task for visual understanding. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  126. Le Guennec A, Malinowski S, Tavenard R (2016) Data augmentation for time series classification using convolutional neural networks. In: ECML/PKDD workshop on advanced analytics and learning on temporal data, Riva del Garda, Italy

  127. Lee HY, Huang JB, Singh M, Yang MH (2017) Unsupervised representation learning by sorting sequences. In: International conference on computer vision (ICCV), Venice, Italy

  128. Levinkov E, Fritz M (2013) Sequential Bayesian model update under structured scene prior for semantic road scenes labeling. In: International conference on computer vision (ICCV), Sydney, Australia

  129. Li K, Li Y, You S, Barnes N (2017) Photo-realistic simulation of road scene for data-driven methods in bad weather. In: Conference on computer vision and pattern recognition workshop (CVPRW), Honolulu, HI, USA

  130. Li W, Duan L, Xu D, Tsang IW (2014) Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. Trans Pattern Anal Mach Intell 36(6):1134–1148

    Article  Google Scholar 

  131. Li Y, Wang N, Shi J, Liu J, Hou X (2016) Revisiting batch normalization for practical domain adaptation. In: International conference on learning representations workshops, Toulon, France

  132. Lin D, Dai J, Jia J, He K, Sun J (2016) ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  133. Lin G, Milan A, Shen C, Reid ID (2017) RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: Conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  134. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision (ECCV), Zurich, Switzerland

  135. Liu B, Ferrari V (2017) Active learning for human pose estimation. In: International conference on computer vision (ICCV), Venice, Italy

  136. Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Advances in neural information processing systems (NIPS), Barcelona, Spain

  137. Liu X, Song L, Wu X, Tan T (2016) Transferring deep representation for NIR-VIS heterogeneous face recognition. In: International conference on biometrics (ICB), Halmstad, Sweden

  138. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  139. Lu H, Zhang L, Cao Z, Wei W, Xian K, Shen C, van den Hengel A (2017) When unsupervised domain adaptation meets tensor representations. In: International conference on computer vision (ICCV), Venice, Italy

  140. Lu Y, Tai YW, Tang CK (2018) Attribute-guided face generation using conditional CycleGAN. In: European conference on computer vision (ECCV), Munich, Germany

  141. Ma F, Cavalheiro GV, Karaman S (2018) Self-supervised sparse-to-dense: self-supervised depth completion from LiDAR and monocular camera. In: International conference on robotics and automation (ICRA), Brisbane, Australia

  142. Maninis KK, Caelles S, Pont-Tuset J, Van Gool L (2017) Deep extreme cut: from extreme points to object segmentation. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  143. Mehrotra A, Dukkipati A (2017) Generative adversarial residual pairwise networks for one shot learning. CoRR. arXiv:1703.08033

  144. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: Advances in neural information processing systems (NIPS), Stateline, NA, USA

  145. Mishra N, Rohaninejad M, Chen X, Abbeel P (2018) A simple neural attentive meta-learner. In: International conference on learning representations (ICLR), New Orleans, LA, USA

  146. Misra I, Lawrence Zitnick C, Mitchell M, Girshick R (2016a) Seeing through the human reporting bias: visual classifiers from noisy human-centric labels. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  147. Misra I, Zitnick CL, Hebert M (2016b) Shuffle and learn: unsupervised learning using temporal order verification. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  148. Natarajan N, Dhillon IS, Ravikumar PK, Tewari A (2013) Learning with noisy labels. In: Advances in neural information processing systems (NIPS), Stateline, NA, USA

  149. Nguyen HV, Ho HT, Patel VM, Chellappa R (2015) Dash-n: joint hierarchical domain adaptation and feature learning. IEEE Trans Image Process 24(12):5479–5491

    Article  Google Scholar 

  150. Noroozi M, Favaro P (2016) Unsupervised learning of visual representations by solving jigsaw puzzles. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  151. Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  152. Owens A, Wu J, McDermott JH, Freeman WT, Torralba A (2016) Ambient sound provides supervision for visual learning. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  153. Pan SJ, Yang Q et al (2010) A survey on transfer learning. Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  154. Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  155. Papadopoulos DP, Uijlings JR, Keller F, Ferrari V (2016) We don’t need no bounding-boxes: training object class detectors using only human verification. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  156. Papadopoulos DP, Uijlings JR, Keller F, Ferrari V (2017) Extreme clicking for efficient object annotation. In: International conference on computer vision (ICCV), Venice, Italy

  157. Papadopoulos DP, Uijlings JR, Keller F, Ferrari V (2017) Training object class detectors with click supervision. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  158. Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: a survey of recent advances. Signal Process Mag 32(3):53–69

    Article  Google Scholar 

  159. Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: feature learning by inpainting. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  160. Pathak D, Girshick RB, Dollár P, Darrell T, Hariharan B (2017) Learning features by watching objects move. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  161. Peng KC, Wu Z, Ernst J (2018) Zero-shot deep domain adaptation. In: European conference on computer vision (ECCV), Munich, Germany

  162. Peng X, Sun B, Ali K, Saenko K (2015) Learning deep object detectors from 3D models. In: International conference on computer vision (ICCV), Santiago, Chile

  163. Pinheiro PO, Collobert R (2015) From image-level to pixel-level labeling with convolutional networks. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  164. Pinto L, Gandhi D, Han Y, Park YL, Gupta A (2016) The curious robot: learning visual representations via physical interactions. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  165. Qiao S, Shen W, Zhang Z, Wang B, Yuille A (2018) Deep co-training for semi-supervised image recognition. In: European conference on computer vision (ECCV), Munich, Germany

  166. Qin J, Liu L, Shao L, Shen F, Ni B, Chen J, Wang Y (2017) Zero-shot action recognition with error-correcting output codes. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  167. Qiu W, Yuille A (2016) UnrealCV: Connecting computer vision to unreal engine. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  168. Rader N, Bausano M, Richards JE (1980) On the nature of the visual-cliff-avoidance response in human infants. Child Dev 51(1):61–68

    Article  CAS  Google Scholar 

  169. Raj A, Namboodiri VP, Tuytelaars T (2015) Subspace alignment based domain adaptation for RCNN detector. In: British machine vision conference (BMVC), Swansea, UK

  170. Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) Squad: 100,000+ questions for machine comprehension of text. In: Conference on empirical methods in natural language processing (EMNLP), Austin, TX, USA

  171. Ratner AJ, Ehrenberg H, Hussain Z, Dunnmon J, Ré C (2017) Learning to compose domain-specific transformations for data augmentation. In: Advances in neural information processing systems, Long Beach, CA, USA

  172. Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: International conference on learning representations (ICLR), Toulon, France

  173. Redko I, Habrard A, Sebban M (2017) In: Theoretical analysis of domain adaptation with optimal transport. In: Joint European conference on machine learning and knowledge discovery in databases (ECML KDD), Skopje, Macedonia

  174. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  175. Reed S, Lee H, Anguelov D, Szegedy C, Erhan D, Rabinovich A (2014) Training deep neural networks on noisy labels with bootstrapping. In: International conference on learning representations workshops, Banff, Canada

  176. Reed S, Akata Z, Lee H, Schiele B (2016) Learning deep representations of fine-grained visual descriptions. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  177. Remez T, Huang J, Brown M (2018) Learning to segment via cut-and-paste. In: European conference on computer vision (ECCV), Munich, Germany

  178. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (NIPS), Montreal, Canada

  179. Richter SR, Vineet V, Roth S, Koltun V (2016) Playing for data: ground truth from computer games. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  180. Richter SR, Hayder Z, Koltun V (2017) Playing for benchmarks. In: International conference on computer vision (ICCV), Venice, Italy

  181. Rippel O, Paluri M, Dollar P, Bourdev L (2016) Metric learning with adaptive density discrimination. In: International conference on learning representations (ICLR), San Juan, Puerto Rico

  182. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI), Munich, Germany

  183. Ros G, Sellart L, Materzynska J, Vazquez D, Lopez AM (2016) The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: The computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  184. Roy N, McCallum A (2001) Toward optimal active learning through monte carlo estimation of error reduction. In: International conference on machine learning (ICML), Williamstown, MA, USA

  185. Roy S, Unmesh A, Namboodiri VP (2018) Deep active learning for object detection. In: British machine vision conference (BMVC), Newcastle, UK

  186. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  Google Scholar 

  187. Russo P, Carlucci FM, Tommasi T, Caputo B (2018) From source to target and back: symmetric bi-directional adaptive GAN. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  188. Sadeghi F, Levine S (2017) CAD2RL: real single-image flight without a single real image. In: Robotics science and systems (RSS), Boston, MA, USA

  189. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision (ECCV), Crete, Greece

  190. Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Conference of the international speech communication association (INTERSPEECH), Singapore

  191. Sakaridis C, Dai D, Van Gool L (2018) Semantic foggy scene understanding with synthetic data. Int J Comput Vis 126:973–992

    Article  Google Scholar 

  192. Salakhutdinov R, Larochelle H (2010) Efficient learning of deep Boltzmann machines. In: International conference on artificial intelligence and statistics (ICAIS), San Diego, CA, USA

  193. Sankaranarayanan S, Balaji Y, Castillo CD, Chellappa R (2018) Generate to adapt: aligning domains using generative adversarial networks. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  194. Scheffer T, Decomain C, Wrobel S (2001) Active hidden Markov models for information extraction. In: International symposium on intelligent data analysis, Berlin, Heidelberg

  195. Schlegl T, Seeböck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging (IPMI), Boone, NC, USA

  196. Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: a unified embedding for face recognition and clustering. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  197. Sener O, Savarese S (2018) Active learning for convolutional neural networks: a core-set approach. In: International conference on learning representations (ICLR), New Orleans, LA, USA

  198. Settles B (2009) Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison

  199. Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034

    Article  Google Scholar 

  200. Shi M, Ferrari V (2016) Weakly supervised object localization using size estimates. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  201. Shimodaira H (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plan Inference 90(2):227–244

    Article  Google Scholar 

  202. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  203. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (ICLR), San Diego, CA, USA

  204. Singh S, Gupta A, Efros AA (2012) Unsupervised discovery of mid-level discriminative patches. In: European conference on computer vision (ECCV), Firenze, Italy

  205. Sivic J, Russell BC, Efros AA, Zisserman A, Freeman WT (2005) Discovering objects and their location in images. In: Computer vision and pattern recognition (CVPR), San Diego, CA, USA

  206. Socher R, Ganjoo M, Manning CD, Ng A (2013) Zero-shot learning through cross-modal transfer. In: Advances in neural information processing systems (NIPS), Stateline, NA, USA

  207. Sohn K, Liu S, Zhong G, Yu X, Yang MH, Chandraker M (2017) Unsupervised domain adaptation for face recognition in unlabeled videos. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  208. Song HO, Girshick R, Jegelka S, Mairal J, Harchaoui Z, Darrell T (2014) On learning to localize objects with minimal supervision. In: International conference on machine learning (ICML), Beijing, China

  209. Song HO, Lee YJ, Jegelka S, Darrell T (2014) Weakly-supervised discovery of visual pattern configurations. In: Advances in neural information processing systems (NIPS), Montreal, Canada

  210. Stavens D, Thrun S (2006) A self-supervised terrain roughness estimator for off-road autonomous driving. In: Uncertainty in artificial intelligence (UAI), Cambridge, MA, USA

  211. Sukhbaatar S, Bruna J, Paluri M, Bourdev L, Fergus R (2014) Training convolutional networks with noisy labels. In: International conference on learning representations workshops, Banff, Canada

  212. Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  213. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems (NIPS), Montreal, Canada

  214. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  215. Taigman Y, Polyak A, Wolf L (2017) Unsupervised cross-domain image generation. In: International conference on learning representations (ICLR), Toulon, France

  216. Tan B, Zhang Y, Pan SJ, Yang Q (2017) Distant domain transfer learning. In: AAAI, San Francisco, CA, USA

  217. Taylor GR, Chosak AJ, Brewer PC (2007) OVVV: using virtual worlds to design and evaluate surveillance systems. In: Computer vision and pattern recognition (CVPR), Minneapolis, MN, USA

  218. Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li L (2016) Yfcc100m: the new data in multimedia research. Commun ACM 59:64–73

    Article  Google Scholar 

  219. Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P (2017) Domain randomization for transferring deep neural networks from simulation to the real world. In: International conference on intelligent robots and systems (IROS), Vancouver, Canada

  220. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: ACM international conference on multimedia (MM), Ottawa, Canada

  221. Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: Computer vision and pattern recognition (CVPR), Colorado Springs, CO, USA

  222. Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  223. Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: International conference on computer vision (ICCV), Santiago, Chile

  224. Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, To T, Cameracci E, Boochoon S, Birchfield S (2018) Training deep networks with synthetic data: bridging the reality gap by domain randomization. In: Computer vision and pattern recognition workshops (CVPRW), Salt Lake City, UT, USA

  225. Tsai YH, Hung WC, Schulter S, Sohn K, Yang MH, Chandraker M (2018) Learning to adapt structured output space for semantic segmentation. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  226. Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. In: Computer vision and pattern recognition (CVPR), Columbus, OH, USA

  227. Tzeng E, Hoffman J, Darrell T, Saenko K (2015) Simultaneous deep transfer across domains and tasks. In: International conference on computer vision (ICCV), Santiago, Chile

  228. Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  229. Van Den Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K (2016) WaveNet: a generative model for raw audio. CoRR. arXiv:1609.03499 (125)

  230. Van Horn G, Branson S, Farrell R, Haber S, Barry J, Ipeirotis P, Perona P, Belongie S (2015) Building a bird recognition app and large scale dataset with citizen scientists: the fine print in fine-grained dataset collection. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  231. Varma G, Subramanian A, Namboodiri A, Chandraker M, Jawahar CV (2019) IDD: A dataset for exploring problems of autonomous navigation in unconstrained environments. In: IEEE Winter conference on applications of computer vision (WACV), Waikoloa, Hawaii

  232. Vazquez D, Lopez AM, Marin J, Ponsa D, Geronimo D (2014) Virtual and real world adaptation for pedestrian detection. Trans Pattern Anal Mach Intell 36(4):797–809

    Article  Google Scholar 

  233. Veit A, Alldrin N, Chechik G, Krasin I, Gupta A, Belongie SJ (2017) Learning from noisy large-scale datasets with minimal supervision. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  234. Vezhnevets A, Buhmann JM, Ferrari V (2012) Active learning for semantic segmentation with expected change. In: Computer vision and pattern recognition (CVPR), Providence, RI, USA

  235. Vijayanarasimhan S, Grauman K (2014) Large-scale live active learning: training object detectors with crawled data and crowds. Int J Comput Vis 108(1–2):97–114

    Article  Google Scholar 

  236. Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. In: Advances in neural information processing systems (NIPS), Barcelona, Spain

  237. Vogt P, Smith ADM (2005) Learning color words is slow: a cross-situational learning account. Behav Brain Sci 28(4):509–510

    Article  Google Scholar 

  238. Wang C, Mahadevan S (2011) Heterogeneous domain adaptation using manifold alignment. In: International joint conference on artificial intelligence (IJCAI), Barcelona, Spain

  239. Wang M, Deng W (2018) Deep visual domain adaptation: a survey. Neurocomputing 312:135–153

    Article  Google Scholar 

  240. Wang X, Gupta A (2015) Unsupervised learning of visual representations using videos. In: International conference on computer vision (ICCV), Santiago, Chile

  241. Wang YX, Hebert M (2016) Learning to learn: model regression networks for easy small sample learning. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  242. Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9

    Article  Google Scholar 

  243. Wu J, Yu Y, Huang C, Yu K (2015) Deep multiple instance learning for image classification and auto-annotation. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  244. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, ukasz Kaiser, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado G, Hughes M, Dean J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. CoRR. arXiv:1609.08144

  245. Xian Y, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B (2016) Latent embeddings for zero-shot classification. In: Computer vision and pattern recognition (CVPR), Las Vegas, NV, USA

  246. Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  247. Xiao T, Xia T, Yang Y, Huang C, Wang X (2015) Learning from massive noisy labeled data for image classification. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  248. Xu J, Schwing AG, Urtasun R (2015) Learning to segment under various forms of weak supervision. In: Computer vision and pattern recognition (CVPR), Boston, MA, USA

  249. Yan H, Ding Y, Li P, Wang Q, Xu Y, Zuo W (2017) Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  250. Yao A, Gall J, Leistner C, Van Gool L (2012) Interactive object detection. In: Computer vision and pattern recognition (CVPR), Providence, RI, USA

  251. Yi Z, Zhang HR, Tan P, Gong M (2017) DualGAN: unsupervised dual learning for image-to-image translation. In: International conference on computer vision (ICCV), Venice, Italy

  252. Yoo D, Fan H, Boddeti VN, Kitani KM (2018) Efficient k-shot learning with regularized deep networks. In: AAAI, New Orleans, LA, USA

  253. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural. networks? In: Advances in neural information processing systems (NIPS), Montreal, Canada

  254. Zhang H, Xu T, Li H, Zhang S, Huang X, Wang X, Metaxas D (2017a) StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: International conference on computer vision (ICCV), Venice, Italy

  255. Zhang J, Ding Z, Li W, Ogunbona P (2018) Importance weighted adversarial nets for partial domain adaptation. In: Computer vision and pattern recognition (CVPR), Salt Lake City, UT, USA

  256. Zhang L, Xiang T, Gong S et al (2017b) Learning a deep embedding model for zero-shot learning. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  257. Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: European conference on computer vision (ECCV), Amsterdam, Netherlands

  258. Zhang R, Isola P, Efros AA (2017c) Split-brain autoencoders: unsupervised learning by cross-channel prediction. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  259. Zhang Y, David P, Gong B (2017d) Curriculum domain adaptation for semantic segmentation of urban scenes. In: International conference on computer vision (ICCV), Venice, Italy

  260. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

  261. Zhu JJ, Bento J (2017) Generative adversarial active learning. In: Advances in neural information processing systems workshops, Long Beach, CA

  262. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International conference on computer vision (ICCV), Venice, Italy

  263. Zhu Y, Chen Y, Lu Z, Pan SJ, Xue GR, Yu Y, Yang Q (2011) Heterogeneous transfer learning for image classification. In: AAAI, San Francisco, California, USA

  264. Zhuang B, Liu L, Li Y, Shen C, Reid ID (2017) Attend in groups: a weakly-supervised deep learning framework for learning from web data. In: Computer vision and pattern recognition (CVPR), Honolulu, HI, USA

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  1. CVIT, IIIT Hyderabad, Hyderabad, India

    Lovish Chum & C. V. Jawahar

  2. Intel, Bangalore, India

    Anbumani Subramanian

  3. IIT Hyderabad, Hyderabad, India

    Vineeth N. Balasubramanian

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Chum, L., Subramanian, A., Balasubramanian, V.N. et al. Beyond Supervised Learning: A Computer Vision Perspective. J Indian Inst Sci 99, 177–199 (2019). https://doi.org/10.1007/s41745-019-0099-3

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