ICML 2020poster47 citations
SGD Learns One-Layer Networks in WGANs
Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis
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.
BibTeX
@InProceedings{pmlr-v119-lei20b,
title = {{SGD} Learns One-Layer Networks in {WGAN}s},
author = {Lei, Qi and Lee, Jason and Dimakis, Alex and Daskalakis, Constantinos},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {5799--5808},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/lei20b/lei20b.pdf},
url = {https://proceedings.mlr.press/v119/lei20b.html},
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.}
}