Disentangling factors of variation in deep representations using adversarial training

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 5047-5055

  Mathieu, Michael ^a   Zhao, Junbo ^a   Sprechmann, Pablo ^a   Ramesh, Aditya ^a   Le Cun, Yann ^a  

^a NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOENCODERS; COMPLEMENTARY CODE; GENERATIVE MODEL; INTRA CLASS; MULTIPLE VIEWPOINTS; REAL DATA SETS; REALISTIC SCENARIO; SINGLE IMAGES;

DATA ACQUISITION;

EID: 85018893392     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Yoshua Bengio. Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1):1-127, 2009.
2. Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, and Bruno A. Olshausen. Discovering hidden factors of variation in deep networks. CoRR, abs/1412.6583, 2014.
3. Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet. Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS Workshop, number EPFL-CONF-192376, 2011.
4. Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS, 2015.
5. Alexey Dosovitskiy, Jost Tobias Springenberg, and Thomas Brox. Learning to generate chairs with convolutional neural networks. CoRR, abs/1411.5928, 2014.
6. Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, and Aaron Courville. Adversarially learned inference. arXivpreprint arXiv:1606.00704, 2016.
7. Harrison Edwards and Amos Storkey. Censoring representations with an adversary. arXiv preprint arXiv:1511.05897, 2015.
8. Athinodoros S Georghiades, Peter N Belhumeur, and David J Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(6):643-660, 2001.
9. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial networks. NIPS, 2014.
10. Geoffrey E. Hinton, Alex Krizhevsky, and Sida D. Wang. Transforming auto-encoders. In Proceedings of the 21th International Conference on Artificial Neural Networks - Volume Part I, ICANN'11, pages 44-51, Berlin, Heidelberg, 2011. Springer-Verlag.
11. Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems, pages 3581-3589, 2014.
12. Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
13. Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum. Deep convolutional inverse graphics network. In Advances in Neural Information Processing Systems, pages 2530-2538, 2015.
14. Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, and Ole Winther. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015.
15. Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
16. Yann LeCun, Fu Jie Huang, and Leon Bottou. Learning methods for generic object recognition with invariance to pose and lighting. In CVPR, 2004.
17. Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. The variational fair autoencoder. ICLR, 2016.
18. Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian J. Goodfellow. Adversarial autoencoders. CoRR, abs/1511.05644, 2015.
19. Michaël Mathieu, Camille Couprie, and Yann LeCun. Deep multi-scale video prediction beyond mean square error. ICLR, abs/1511.05440, 2015.
20. Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
21. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, abs/1511.06434, 2015.
22. Marc'Aurelio Ranzato, Fu-Jie Huang, Y-Lan Boureau, and Yann LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In Proc. Computer Vision and Pattern Recognition Conference (CVPR'07). IEEE Press, 2007.
23. Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee. Learning to disentangle factors of variation with manifold interaction. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1431-1439, 2014.
24. Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 1252-1260. Curran Associates, Inc., 2015.
25. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082, 2014.
26. Joshua B. Tenenbaum and William T. Freeman. Separating style and content with bilinear models. Neural Comput., 12(6):1247-1283, June 2000.
27. Lucas Theis, Aäron van den Oord, and Matthias Bethge. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844, 2015.
28. Junbo Zhao, Michael Mathieu, Ross Goroshin, and Yann LeCun. Stacked what-where auto-encoders. In ICLR workshop submission, 2016.