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Some Theoretical Properties of GANs

Abstract : Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the art samples, especially in the domain of image creation. The fundamental principle of GANs is to approximate the unknown distribution of a given data set by optimizing an objective function through an adversarial game between a family of generators and a family of discriminators. In this paper, we offer a better theoretical understanding of GANs by analyzing some of their mathematical and statistical properties. We study the deep connection between the adversarial principle underlying GANs and the Jensen-Shannon divergence, together with some optimality characteristics of the problem. An analysis of the role of the discriminator family via approximation arguments is also provided. In addition, taking a statistical point of view, we study the large sample properties of the estimated distribution and prove in particular a central limit theorem. Some of our results are illustrated with simulated examples.
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Contributor : Biau Gérard <>
Submitted on : Tuesday, March 20, 2018 - 9:52:46 AM
Last modification on : Thursday, April 16, 2020 - 12:16:02 PM
Document(s) archivé(s) le : Tuesday, September 11, 2018 - 9:27:49 AM


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  • HAL Id : hal-01737975, version 1
  • ARXIV : 1803.07819


Gérard Biau, Benoît Cadre, M. Sangnier, U. Tanielian. Some Theoretical Properties of GANs. 2018. ⟨hal-01737975⟩



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