Improving Generalization and Stability of Generative Adversarial NetworksDownload PDF

Published: 21 Dec 2018, Last Modified: 29 Sept 2024ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.
Keywords: GAN, generalization, gradient penalty, zero centered, convergence
TL;DR: We propose a zero-centered gradient penalty for improving generalization and stability of GANs
Code: [![github](/images/github_icon.svg) htt210/GeneralizationAndStabilityInGANs](https://github.com/htt210/GeneralizationAndStabilityInGANs)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/improving-generalization-and-stability-of/code)
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