3 years ago

Cramer-Wold AutoEncoder.

Stanisław Jastrzębski, Marcin Mazur, Szymon Knop, Przemysław Spurek, Igor Podolak, Jacek Tabor

We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Following WAE, we directly encourage normality of the latent space. Our paper uses also the recent idea from Sliced WAE (SWAE) model, which uses one-dimensional projections as a method of verifying closeness of two distributions.

The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE. We show that the Cramer-Wold metric between Gaussian mixtures is given by a simple analytic formula, which results in the removal of sampling necessary to estimate the cost function in WAE and SWAE models.

As a consequence, while drastically simplifying the optimization procedure, CWAE produces samples of a matching perceptual quality to other SOTA models.

Publisher URL: http://arxiv.org/abs/1805.09235

DOI: arXiv:1805.09235v1

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