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Adversarial formulations such as generative adversarial networks (GANs) have rekindled interest in two-player min-max games.
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Improved techniques for training gans
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Improved training of wasserstein gans
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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
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The numerics of gans
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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Large scale gan training for high fidelity natural image synthesis
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Training gans with optimism
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Learning with opponent-learning awareness
Jakob Foerster, Richard Y Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, and Igor Mordatch · 2018
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Global convergence to the equilibrium of gans using variational inequalities
Competitive gradient descent
Florian Schäfer and Anima Anandkumar · 2019
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Acceleration via symplectic discretization of high-resolution differential equations
Bin Shi, Simon S Du, Weijie Su, and Michael I Jordan · 2019
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On solving minimax optimization locally: A follow-the-ridge approach
Yuanhao Wang, Guodong Zhang, and Jimmy Ba · 2019
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The unusual effectiveness of averaging in gan training, 2019
Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, and Vijay Chandrasekhar · 2019
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Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, and Simon Lacoste-Julien · 2018
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Chris J Maddison, Daniel Paulin, Yee Whye Teh, Brendan O’Donoghue, and Arnaud Doucet · 2018
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Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Last-iterate convergence rates for min-max optimization
Jacob Abernethy, Kevin A Lai, and Andre Wibisono · 2019
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Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems
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The limits of min-max optimization algorithms: convergence to spurious non-critical sets
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Linear lower bounds and conditioning of differentiable games
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Training generative adversarial networks by solving ordinary differential equations
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Local convergence analysis of gradient descent ascent with finite timescale separation
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Rebooting acgan: Auxiliary classifier gans with stable training
Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park · 2021
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Vitgan: Training gans with vision transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, and Ce Liu · 2021
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Discretization drift in two-player games
Mihaela C Rosca, Yan Wu, Benoit Dherin, and David Barrett · 2021
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On the suboptimality of negative momentum for minimax optimization
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Near-optimal local convergence of alternating gradient descent-ascent for minimax optimization
Guodong Zhang, Yuanhao Wang, Laurent Lessard, and Roger B Grosse · 2022
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