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Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task.
Equilibrium points in n-person games
Nash, J. F · 1950
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I · 2010
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
Krizhevsky, A., Nair, V., and Hinton, G · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
Generalization and equilibrium in generative adversarial nets (gans)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Earlier work this paper cites.
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.
Adagan: Boosting generative models
Tolstikhin, I. O., Gelly, S., Bousquet, O., Simon-Gabriel, C., and Schölkopf, B · 2017
Cited alongside, same era.
Training gans with optimism
Daskalakis, C., Ilyas, A., Syrgkanis, V., and Zeng, H · 2018
Cited alongside, same era.
A convex duality framework for gans
Farnia, F. and Tse, D · 2018
Cited alongside, same era.
Are gans created equal? A large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
Cited alongside, same era.
On the convergence of competitive, multi-agent gradient-based learning
Mazumdar, E. and Ratliff, L. J · 2018
Cited alongside, same era.
Finding mixed nash equilibria of generative adversarial networks
Hsieh, Y., Liu, C., and Cevher, V · 2019
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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On finding local nash equilibria (and only local nash equilibria) in zero-sum games
Mazumdar, E. V., Jordan, M. I., and Sastry, S. S · 2019
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Solving a class of non-convex min-max games using iterative first order methods
Nouiehed, M., Sanjabi, M., Huang, T., Lee, J. D., and Razaviyayn, M · 2019
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Torchgan: A flexible framework for gan training and evaluation
Pal, A. and Das, A · 2019
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I. J., Metaxas, D. N., and Odena, A · 2019
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Cited alongside, same era.
Skill rating for generative models
Olsson, C., Bhupatiraju, S., Brown, T. B., Odena, A., and Goodfellow, I. J · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
Sajjadi, M. S. M., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
Cited alongside, same era.
Local saddle point optimization: A curvature exploitation approach
Adolphs, L., Daneshmand, H., Lucchi, A., and Hofmann, T · 2019
Cited alongside, same era.
Pros and cons of gan evaluation measures
Borji, A · 2019
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Cited alongside, same era.
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Policy optimization provably converges to nash equilibria in zero-sum linear quadratic games
Zhang, K., Yang, Z., and Basar, T · 2019
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A closer look at the optimization landscapes of generative adversarial networks
Berard, H., Gidel, G., Almahairi, A., Vincent, P., and Lacoste-Julien, S · 2020
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Do gans always have nash equilibria?
Farnia, F. and Ozdaglar, A. E · 2020
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What is local optimality in nonconvex-nonconcave minimax optimization?
Jin, C., Netrapalli, P., and Jordan, M. I · 2020
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On gradient descent ascent for nonconvex-concave minimax problems
Lin, T., Jin, C., and Jordan, M. I · 2020
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A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach
Mokhtari, A., Ozdaglar, A. E., and Pattathil, S · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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On duality gap as a measure for monitoring gan training
Sidheekh, S., Aimen, A., Madan, V., and Krishnan, N. C · 2020
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