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Main Authors: Yang, Hongkang, E, Weinan
Format: Preprint
Published: 2021
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Online Access:https://arxiv.org/abs/2107.03633
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author Yang, Hongkang
E, Weinan
author_facet Yang, Hongkang
E, Weinan
contents The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In particular, GAN is vulnerable to the memorization phenomenon, the eventual convergence to the empirical distribution. We consider a simplified GAN model with the generator replaced by a density, and analyze how the discriminator contributes to generalization. We show that with early stopping, the generalization error measured by Wasserstein metric escapes from the curse of dimensionality, despite that in the long term, memorization is inevitable. In addition, we present a hardness of learning result for WGAN.
format Preprint
id arxiv_https___arxiv_org_abs_2107_03633
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Generalization Error of GAN from the Discriminator's Perspective
Yang, Hongkang
E, Weinan
Machine Learning
68T07, 62G07, 60-08
The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In particular, GAN is vulnerable to the memorization phenomenon, the eventual convergence to the empirical distribution. We consider a simplified GAN model with the generator replaced by a density, and analyze how the discriminator contributes to generalization. We show that with early stopping, the generalization error measured by Wasserstein metric escapes from the curse of dimensionality, despite that in the long term, memorization is inevitable. In addition, we present a hardness of learning result for WGAN.
title Generalization Error of GAN from the Discriminator's Perspective
topic Machine Learning
68T07, 62G07, 60-08
url https://arxiv.org/abs/2107.03633