First published: 2017/01/26 (7 years ago) 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.
This very new paper, is currently receiving quite a bit of attention by the [community](https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/).
The paper describes a new training approach, which solves the two major practical problems with current GAN training:
1) The training process comes with a meaningful loss. This can be used as a (soft) performance metric and will help debugging, tune parameters and so on.
2) The training process does not suffer from all the instability problems. In particular the paper reduces mode collapse significantly.
On top of that, the paper comes with quite a bit mathematical theory, explaining why there approach works and other approachs have failed. This paper is a must read for anyone interested in GANs.
