DeLiGAN : Generative adversarial networks for diverse and limited data

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 4941-4949

  Gurumurthy, Swaminathan ^a   Sarvadevabhatla, Ravi Kiran ^a   Babu, R Venkatesh ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DRAWING (GRAPHICS); MIXTURES;

ADVERSARIAL NETWORKS; HAND-DRAWN SKETCHES; HANDWRITTEN DIGIT; HUMAN ASSESSMENT; IMAGE MODALITY; LIMITED TRAINING DATA; REALISTIC IMAGES; SIMPLE MODIFICATIONS;

PATTERN RECOGNITION;

EID: 85040691866     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.525     Document Type: Conference Paper

References (25)

1. K. Arulkumaran, A. Creswell, and A. A. Bharath. Improving sampling from generative autoencoders with markov chains. arXiv preprint arXiv:1610.09296, 2016.
2. Y. Bengio, G. Mesnil, Y. Dauphin, and S. Rifai. Better mixing via deep representations. In ICML (1), volume 28 of JMLR Workshop and Conference Proceedings, pages 552-560, 2013.
3. Y. Burda, R. Grosse, and R. Salakhutdinov. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519, 2015.
4. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657, 2016.
5. E. L. Denton, S. Chintala, R. Fergus, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pages 1486-1494, 2015.
6. M. Eitz, J. Hays, and M. Alexa. How do humans sketch objects? ACM Transactions on Graphics (TOG), 31(4):44, 2012.
7. S. Gershman, M. Hoffman, and D. Blei. Nonparametric variational inference. arXiv preprint arXiv:1206.4665, 2012.
8. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672-2680, 2014.
9. T. Han, Y. Lu, S.-C. Zhu, and Y. N. Wu. Alternating back-propagation for generator network. arXiv preprint arXiv:1606.08571, 2016.
10. T. S. Jaakkola and M. I. Jordan. Improving the mean field approximation via the use of mixture distributions. In Learning in graphical models, pages 163-173. Springer, 1998.
11. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine learning, 37(2):183-233, 1999.
12. D. P. Kingma, T. Salimans, and M. Welling. Improving variational inference with inverse autoregressive flow. arXiv preprint arXiv:1606.04934, 2016.
13. D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
14. A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
16. Y. Lecun and C. Cortes. The MNIST database of handwritten digits.
17. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
18. S. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee. Learning what and where to draw. arXiv preprint arXiv:1610.02454, 2016.
19. D. J. Rezende and S. Mohamed. Variational inference with normalizing flows. arXiv preprint arXiv:1505.05770, 2015.
20. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211-252, 2015.
21. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training GANs. arXiv preprint arXiv:1606.03498, 2016.
22. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
23. L. Theis, A. v. d. Oord, and M. Bethge. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844, 2015.
24. J. Zhao, M. Mathieu, and Y. LeCun. Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126, 2016.
25. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In Advances in neural information processing systems, pages 487-495, 2014.