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Neural Information Processing Systems, pp. 2292–2300 (2013)
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wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777
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87. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative
adversarial networks. arXiv preprint arXiv:1802.05957 (2018)
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89. Tolstikhin, I., Bousquet, O., Gelly, S., Schoelkopf, B.: Wasserstein auto-encoders. arXiv
preprint arXiv:1711.01558 (2017)
90. Dinh, L., Krueger, D., Bengio, Y.: Nice: Non-linear independent components estimation.
arXiv preprint arXiv:1410.8516 (2014)
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performance under visual distortions. In: 26th International Conference on Computer Communication and Networks (ICCCN), pp. 1–7. IEEE (2017)
93. Barratt, S., Sharma, R.: A note on the inception score. arXiv preprint arXiv:1801.01973
(2018)
94. 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)
95. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a
two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural
Information Processing Systems, pp. 6626–6637 (2017)