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) [1] . 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) [2] . 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.
In REF , the authors adopt the Wasserstein distance in the latent space to measure the distance between the distribution of the latent code and the given one and generate images with better quality.
Wasserstein Auto-Encoders
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