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arXiv · 1910.07030

SGD Learns One-Layer Networks in WGANs

Qi Lei·Jason D. Lee·Alexandros G. Dimakis·Constantinos Daskalakis
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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 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.

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Qi Lei, Jason D. Lee, Alexandros G. Dimakis, Constantinos Daskalakis. 2019-10-15. SGD Learns One-Layer Networks in WGANs. https://arxiv.org/abs/1910.07030

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