Sliced Generative Models

Szymon Knop,

Marcin Mazur,

Jacek Tabor,

Igor T. Podolak,

Przemysław Spurek


In this paper we discuss a class of  AutoEncoder based generative models based on one dimensional sliced approach. The idea is based on the reduction of the discrimination between samples to one-dimensional case.

Our experiments show that methods can be divided into two groups. First consists of methods which are a modification of standard normality tests, while the second is based on classical distances between samples.

It turns out that both groups are correct generative models, but the second one gives a slightly faster decrease rate of Frechet Inception Distance (FID).

Słowa kluczowe: Generative model, AutoEncoder, Wasserstein distances

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