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Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics.
Nash convergence of gradient dynamics in general-sum games
Satinder Singh, Michael Kearns, and Yishay Mansour · 2000
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Discrete dynamical systems
Oded Galor · 2007
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Implicit online learning
Brian Kulis and Peter L Bartlett · 2010
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Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2013
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Optimization, learning, and games with predictable sequences
Sasha Rakhlin and Karthik Sridharan · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Connecting generative adversarial networks and actor-critic methods
David Pfau and Oriol Vinyals · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Do gans actually learn the distribution? an empirical study
Sanjeev Arora and Yi Zhang · 2017
Cited alongside, same era.
Theoretical limitations of encoder-decoder gan architectures
Sanjeev Arora, Andrej Risteski, and Yi Zhang · 2017
Cited alongside, same era.
Saddle-point dynamics: conditions for asymptotic stability of saddle points
Ashish Cherukuri, Bahman Gharesifard, and Jorge Cortes · 2017
Cited alongside, same era.
Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
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Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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Stabilizing adversarial nets with prediction methods
Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, and Tom Goldstein · 2017
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Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
How well can generative adversarial networks learn densities: A nonparametric view
Tengyuan Liang · 2017
Cited alongside, same era.
Last-iterate convergence: Zero-sum games and constrained min-max optimization
Constantinos Daskalakis and Ioannis Panageas
Cited in the paper.
The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas
Cited in the paper.
Later among the works it cites.
On gradient regularizers for mmd gans
Michael Arbel, Dougal J Sutherland, Mikołaj Bińkowski, and Arthur Gretton · 2018
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On how well generative adversarial networks learn densities: Nonparametric and parametric results
Tengyuan Liang · 2018
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The inductive bias of restricted f-gans
Shuang Liu and Kamalika Chaudhuri · 2018
Closest in time.