---
abstract: 'In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.  Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded.  Moreover, some recent works, such as the Bayesian GAN, can be re-interpreted based on our theoretical insight from privacy protection.  Quantitatively, to evaluate the information leakage of well-trained GAN models, we perform various membership attacks on these models.  The results show that previous Lipschitz regularization techniques are effective in not only reducing the generalization gap but also alleviating the information leakage of the training dataset.' 
author:
- |
    Bingzhe Wu$^{1,2}$,  Shiwan Zhao$^{3,*}$, ChaoChao Chen$^{2}\thanks{Co-second author}$,  Haoyang Xu$^{1}$\
    **Li Wang**$^{2}$, **Xiaolu Zhang**$^{2}$, **Guangyu Sun**$^{1}$, **Jun Zhou**$^{2}$\
    $^{1}$Peking University, $^{2}$Ant Financial, $^{3}$IBM Research\
    {wubingzhe, xuhaoyang, gsun}@pku.edu.cn\
    {zhaosw}@cn.ibm.com\
    {chaochao.ccc, aymond.wangl, yueyin.zxl, jun.zhoujun}@antfin.com
bibliography:
- 'nips19.bib'
title: 'Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection'
---

Introduction
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Related Work
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Theoretical Analysis
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Quantitative Analysis
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Conclusion {#sec:con}
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In this paper, we have shown a new perspective of privacy protection to understand the generalization properties of GANs.  Specifically, we have validated the relationship between the generalization gap and the information leakage of the training dataset both theoretically and quantitatively.  This new perspective can also help us to understand the effectiveness of previous techniques on Lipschitz regularizations and the Bayesian GAN.  We hope our work can light the following researchers to leverage our new perspective to design GANs with better generalization ability while preventing the information leakage. 

Appendix
========
