Fall 2020

SGD Learns One-Layer Networks in WGANs

Wednesday, December 16th, 2020 9:00 am9:30 am

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Qi Lei (Princeton University)

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 min-max optimization problem to global optimality but are in practice successfully trained using stochastic gradient descent-ascent. In this talk, 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.