Sliced Generative Models

• Autorzy: Knop Szymon , Mazur Marcin , Tabor Jacek , Podolak Igor T. , Spurek Przemysław

Identyfikatory

DOI

10.4467/20838476SI.18.006.10411

• Języki publikacji: EN

Abstrakty

EN

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 Fréchet Inception Distance (FID).

Słowa kluczowe

EN

generative model; AutoEncoder; Wasserstein distance

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2018
• Tom: Vol. 27
• Strony: 69--79
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Knop Szymon

• Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland

autor

Mazur Marcin

• Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland

autor

Tabor Jacek

• Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland

autor

Podolak Igor T.

• Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland

autor

Spurek Przemysław

• Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland

Bibliografia

• [1] H. Cramér and H. Wold. Some theorems on distribution functions. London Math.Soc., 11:290-294, 1936.
• [2] M. Hazewinkel, ed. Kolmogorov-Smirnov test. Encyclopedia of Mathematics. Springer Science+Business Media B.V. / Kluwer Academic Publishers, 2001.
• [3] N. Henze. Invariant tests for multivariate normality: a critical review. Statist. Papers, 43(4):467 506, 2002.
• [4] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter. Gans trained by a two time-scale update rule converge to a nash equilibrium. arXiv:1706.08500, 2017.
• [5] D.P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv:1312.6114, 2014.
• [6] S. Kolouri, P.E. Pope, C.E. Martin, and G.K. Rohde. Sliced wasserstein autoencoders. 2018.
• [7] M. Mazur and P. Kościelniak. On some goodness of fit tests for normality based on the optimal transport distance. submitted.
• [8] A. Palmer, D. Dey, and J. Bi. Reforming generative autoencoders via goodnessof-fit hypothesis testing. UAI, 2018.
• [9] B.W. Silverman. Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability. Chapman & Hall, London, 1986.
• [10] Jacek Tabor, Szymon Knop, Przemysªaw Spurek, Igor Podolak, Marcin Mazur, and Stanisªaw Jastrzębski. 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.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).