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Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model
[article]
2018
arXiv
pre-print
In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances. We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution. In short, we regularize the
arXiv:1804.01947v3
fatcat:qzqvu76csjeidav45orf7uyq2q