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 dierent
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-enc
(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.