SGD Learns One-Layer Networks in WGANs
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.
Cite
Text
Lei et al. "SGD Learns One-Layer Networks in WGANs." International Conference on Machine Learning, 2020.Markdown
[Lei et al. "SGD Learns One-Layer Networks in WGANs." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/lei2020icml-sgd/)BibTeX
@inproceedings{lei2020icml-sgd,
title = {{SGD Learns One-Layer Networks in WGANs}},
author = {Lei, Qi and Lee, Jason and Dimakis, Alex and Daskalakis, Constantinos},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {5799-5808},
volume = {119},
url = {https://mlanthology.org/icml/2020/lei2020icml-sgd/}
}