Sliced Generative Models

Szymon Knop,

Marcin Mazur,

Jacek Tabor,

Igor T. Podolak,

Przemysław Spurek

Abstrakt

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
References

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[7] M. Mazur and P. Ko±cielniak. On some goodness of t tests for normality based on the optimal transport distance. submitted. [8] A. Palmer, D. Dey, and J. Bi. Reforming generative autoencoders via goodnessoft hypothesis testing. UAI, 2018.

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[10] Jacek Tabor, Szymon Knop, Przemysªaw Spurek, Igor Podolak, Marcin Mazur, and Stanisław Jastrz¦bski. Cramer-wold autoencoder. arXiv preprint arXiv:1805.09235, 2018.

[11] I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf. Wasserstein autoencoders. arXiv preprint arXiv:1711.01558, 2017.

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