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In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function.
Optimal Transport: Old and New , volume 338
Villani, C · 2008
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Smolley, S. P · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2016
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2017
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
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The Cramer distance as a solution to biased Wasserstein gradients
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., and Munos, R · 2017
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Many paths to equilibrium: GANs do not need to decrease divergence at every step
Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A. M., Mohamed, S., and Goodfellow, I · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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On the regularization of Wasserstein GANs
Petzka, H., Fischer, A., and Lukovnicov, D · 2017
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Loss-sensitive generative adversarial networks on lipschitz densities
Qi, G.-J · 2017
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Coulomb GANs: Provably optimal nash equilibria via potential fields
Unterthiner, T., Nessler, B., Klambauer, G., Heusel, M., Ramsauer, H., and Hochreiter, S · 2017
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Stabilizing adversarial nets with prediction methods
Yadav, A., Shah, S., Xu, Z., Jacobs, D., and Goldstein, T · 2017
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Kodali, N., Abernethy, J., Hays, J., and Kira, Z · 2017
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Lim, J. H. and Ye, J. C · 2017
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Approximation and convergence properties of generative adversarial learning
Liu, S., Bousquet, O., and Chaudhuri, K · 2017
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Are GANs created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2017
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The numerics of GANs
Mescheder, L., Nowozin, S., and Geiger, A · 2017
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Fisher GAN
Mroueh, Y. and Sercu, T · 2017
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Mroueh, Y., Li, C., Sercu, T., Raj, A., and Cheng, Y · 2017
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Adler, J. and Lunz, S · 2018
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A convex duality framework for GANs
Farnia, F. and Tse, D · 2018
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Which training methods for GANs do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Is generator conditioning causally related to GAN performance?
Odena, A., Buckman, J., Olsson, C., Brown, T. B., Olah, C., Raffel, C., and Goodfellow, I · 2018
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2018
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