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Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality.
Adversarial training for free!
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Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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M. Arjovsky and L. Bottou · 2017
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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V. Khrulkov and I. V. Oseledets · 2018
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T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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H. Petzka, A. Fischer, and D. Lukovnikov · 2018
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Improving the improved training of wasserstein gans: A consistency term and its dual effect
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Banach wasserstein GAN
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Regularisation of neural networks by enforcing lipschitz continuity
H. Gouk, E. Frank, B. Pfahringer, and M. J. Cree · 2018
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Lipschitz generative adversarial nets
Z. Zhou, J. Liang, Y. Song, L. Yu, H. Wang, W. Zhang, Y. Yu, and Z. Zhang
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X. Wei, B. Gong, Z. Liu, W. Lu, and L. Wang · 2018
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An empirical study on evaluation metrics of generative adversarial networks
Q. Xu, G. Huang, Y. Yuan, C. Guo, Y. Sun, F. Wu, and K. Q. Weinberger · 2018
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Sorting out lipschitz function approximation
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Large scale GAN training for high fidelity natural image synthesis
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Wasserstein of wasserstein loss for learning generative models
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Wasserstein adversarial examples via projected sinkhorn iterations
E. Wong, F. R. Schmidt, and J. Z. Kolter · 2019
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