Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf
Abstract
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE). This regularizer encourages the encoded training distribution to match the prior. We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE). Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality.
BibTeX
@inproceedings{
tolstikhin2018wasserstein,
title={Wasserstein Auto-Encoders},
author={Ilya Tolstikhin and Olivier Bousquet and Sylvain Gelly and Bernhard Schoelkopf},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=HkL7n1-0b},
}