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We study the effect of the stochastic gradient noise on the training ofgenerative adversarial networks (GANs) and show that it can prevent theconvergence of standard game optimization methods, while the batch versionconverges. We address this issue with a novel stochastic variance-reducedextragradient (SVRE) optimization algorithm, which for a large class of gamesimproves upon the previous convergence rates proposed in the literature. Weobserve empirically that SVRE performs similarly to a batch method on MNISTwhile being computationally cheaper, and that SVRE yields more stable GANtraining on standard datasets.

Reducing Noise in GAN Training with Variance Reduced Extragradient