Are Gans created equal? A large-scale study

Advances in Neural Information Processing Systems

Volumn 2018-December, Issue , 2018, Pages 700-709

  Lucic, Mario ^a   Kurach, Karol ^a   Michalski, Marcin ^a   Bousquet, Olivier ^a   Gelly, Sylvain ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADVERSARIAL NETWORKS; COMPUTATIONAL BUDGET; EVALUATION MEASURES; HYPER-PARAMETER OPTIMIZATIONS; LARGE-SCALE STUDIES; OBJECTIVE EVALUATION; PRECISION AND RECALL; RESEARCH ACTIVITIES;

BUDGET CONTROL;

EID: 85063266387     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Martín Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In International Conference on Machine Learning (ICML), 2017.
2. Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. Generalization and equilibrium in generative adversarial nets (GANs). In International Conference on Machine Learning (ICML), 2017.
3. Sanjeev Arora, Andrej Risteski, and Yi Zhang. Do GANs learn the distribution? some theory and empirics. In International Conference on Learning Representations (ICLR), 2018.
4. Philip Bachman and Doina Precup. Variational generative stochastic networks with collaborative shaping. In International Conference on Machine Learning (ICML), 2015.
5. David Berthelot, Tom Schumm, and Luke Metz. BEGAN: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717, 2017.
6. Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), 2016.
7. William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, and Ian Goodfellow. Many paths to equilibrium: GANs do not need to decrease a divergence at every step. In International Conference on Learning Representations (ICLR), 2018.
8. Holly E Gerhard, Felix A Wichmann, and Matthias Bethge. How sensitive is the human visual system to the local statistics of natural images? PLoS computational biology, 9(1), 2013.
9. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), 2014.
10. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Improved training of Wasserstein gans. In Advances in Neural Information Processing Systems (NIPS), 2017.
11. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Advances in Neural Information Processing Systems, 2017.
12. Ferenc Huszár. How (not) to train your generative model: Scheduled sampling, likelihood, adversary? arXiv preprint arXiv:1511.05101, 2015.
13. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR), 2015.
14. Diederik P Kingma and Max Welling. Auto-encoding variational Bayes. International Conference on Learning Representations (ICLR), 2014.
15. Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. On convergence and stability of GANs. arXiv preprint arXiv:1705.07215, 2017.
16. Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. The GAN Landscape: Losses, architectures, regularization, and normalization. arXiv preprint arXiv:1807.04720, 2018.
17. Aravindh Mahendran and Andrea Vedaldi. Understanding deep image representations by inverting them. In Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
18. Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In International Conference on Computer Vision (ICCV), 2017.
19. Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. The numerics of GANs. In Advances in Neural Information Processing Systems (NIPS), 2017.
20. Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
21. Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier GANs. In International Conference on Machine Learning (ICML), 2017.
22. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
23. Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. Assessing generative models via precision and recall. In Advances in Neural Information Processing Systems (NIPS), 2018.
24. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training GANs. In Advances in Neural Information Processing Systems (NIPS), 2016.
25. Lucas Theis, Aäron van den Oord, and Matthias Bethge. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844, 2015.
26. Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse. On the quantitative analysis of decoder-based generative models. International Conference on Learning Representations (ICLR), 2017.
27. Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. International Conference on Computer Vision (ICCV), 2017.