Task: Summarization
Input (English): 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.
Output (English): Attempts to prove that the Stochastic Gradient Decent-Ascent could converge to a global solution for the min-max problem of WGAN.