Wasserstein GAN
Machine Learning
2017-12-08 v3 Machine Learning
Abstract
We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.
Cite
@article{arxiv.1701.07875,
title = {Wasserstein GAN},
author = {Martin Arjovsky and Soumith Chintala and Léon Bottou},
journal= {arXiv preprint arXiv:1701.07875},
year = {2017}
}