2019
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
NeurIPS 2019poster
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…