title: SGD Learns One-Layer Networks in WGANs
abstract: Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax problem to global optimality, but in practice, are successfully trained with stochastic gradient descent-ascent. In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution in polynomial time and sample complexity.
authors: Qi Lei Jason D. Lee Alexandros G. Dimakis Constantinos Daskalakis
hIndex: Alexandros G. Dimakis 55