Wasserstein Auto-Encoders
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, as measured by the FID score.
Cite
@article{arxiv.1711.01558,
title = {Wasserstein Auto-Encoders},
author = {Ilya Tolstikhin and Olivier Bousquet and Sylvain Gelly and Bernhard Schoelkopf},
journal= {arXiv preprint arXiv:1711.01558},
year = {2019}
}
Comments
Published at ICLR 2018.. Included much wider hyperparameter sweep: in significant improvements in FIDs on CelebA