SGD Learns One-Layer Networks in WGANs

Understand

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 optimization problem to global optimality, but are in practice successfully trained using 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 with polynomial time and sample complexity.