  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.
