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Generative adversarial networks (GANs) are a widely used framework forlearning generative models. Wasserstein GANs (WGANs), one of the mostsuccessful variants of GANs, require solving a minmax optimization problem toglobal optimality, but are in practice successfully trained using stochasticgradient descent-ascent. In this paper, we show that, when the generator is aone-layer network, stochastic gradient descent-ascent converges to a globalsolution with polynomial time and sample complexity.

SGD Learns One-Layer Networks in WGANs