Abstract
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples (x) conditioned on both latent variables (z) and known auxiliary information (c). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles z and c in the generation process and provides an encoder that learns inverse mappings from x to both z and c, trained jointly with the generator and the discriminator. We present crucial techniques for training BiCoGANs, which involve an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based cGANs, BiCoGANs encode c more accurately, and utilize z and c more effectively and in a more disentangled way to generate samples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aubry, M., Maturana, D., Efros, A., Russell, B.C., Sivic, J.: Seeing 3D chairs: exemplar part-based 2D–3D alignment using a large dataset of CAD models, June 2014
Belharbi, S., Hérault, R., Chatelain, C., Adam, S.: Deep multi-task learning with evolving weights. In: European Symposium on Artificial Neural Networks (ESANN) (2016)
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: International Conference on Learning Representations (2017)
Dumoulin, V., et al.: Adversarially learned inference. In: International Conference on Learning Representations (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gurumurthy, S., Kiran Sarvadevabhatla, R., Venkatesh Babu, R.: DeLiGAN : generative adversarial networks for diverse and limited data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Huang, S., Ramanan, D.: Expecting the unexpected: training detectors for unusual pedestrians with adversarial imposters. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: Stacked generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Kaneko, T., Hiramatsu, K., Kashino, K.: Generative attribute controller with conditional filtered generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (2014)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV), December 2015
Mahasseni, B., Lam, M., Todorovic, S.: Unsupervised video summarization with adversarial LSTM networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. In: International Conference on Learning Representations (2016). http://arxiv.org/abs/1511.05644
Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: Disentangling factors of variation in deep representation using adversarial training. In: Advances in Neural Information Processing Systems, pp. 5040–5048 (2016)
Mescheder, L., Nowozin, S., Geiger, A.: Adversarial variational Bayes: unifying variational autoencoders and generative adversarial networks. In: International Conference on Machine Learning (ICML) (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (2015)
Perarnau, G., van de Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible conditional GANs for image editing. In: NIPS Workshop on Adversarial Training (2016)
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text-to-image synthesis. In: Proceedings of The 33rd International Conference on Machine Learning (2016)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for Training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Sixt, L., Wild, B., Landgraf, T.: RenderGAN: generating realistic labeled data. arXiv preprint arXiv:1611.01331 (2016)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Vondrick, C., Torralba, A.: Generating the future with adversarial transformers. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Wan, C., Probst, T., Van Gool, L., Yao, A.: Crossing Nets: combining GANs and VAEs with a shared latent space for hand pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Wang, J., Cheng, Y., Feris, R.S.: Walk and learn: facial attribute representation learning from egocentric video and contextual data. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2295–2304 (2016)
Yang, J., Kannan, A., Batra, D., Parikh, D.: LR-GAN: layered recursive generative adversarial networks for image generation. In: International Conference on Learning Representations (2017)
Acknowledgements
This work is based on research sponsored by the Defense Advanced Research Projects Agency under agreement number FA8750-16-2-0204. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jaiswal, A., AbdAlmageed, W., Wu, Y., Natarajan, P. (2019). Bidirectional Conditional Generative Adversarial Networks. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-20893-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20892-9
Online ISBN: 978-3-030-20893-6
eBook Packages: Computer ScienceComputer Science (R0)