Wasserstein GAN Wasserstein GAN
Paper summary 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.
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Wasserstein GAN
Martin Arjovsky and Soumith Chintala and Léon Bottou
arXiv e-Print archive - 2017 via arXiv
Keywords: stat.ML, cs.LG

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