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We examine two different techniques for parameter averaging in GAN training.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
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Improved techniques for training GANs
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Wasserstein generative adversarial networks
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Ian J. Goodfellow · 2017
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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 Nash equilibrium
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Averaging weights leads to wider optima and better generalization
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Cycles in adversarial regularized learning
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Which training methods for GANs do actually converge?
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Multi-agent learning in network zero-sum games is a Hamiltonian system
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Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
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