  We study the effect of the stochastic gradient noise on the training of
generative adversarial networks (GANs) and show that it can prevent the
convergence of standard game optimization methods, while the batch version
converges. We address this issue with a novel stochastic variance-reduced
extragradient (SVRE) optimization algorithm, which for a large class of games
improves upon the previous convergence rates proposed in the literature. We
observe empirically that SVRE performs similarly to a batch method on MNIST
while being computationally cheaper, and that SVRE yields more stable GAN
training on standard datasets.
