Poster
Improving Generalization and Stability of Generative Adversarial Networks
Hoang Thanh-Tung · Truyen Tran · Svetha Venkatesh
Great Hall BC #3
Keywords: [ zero centered ] [ convergence ] [ generalization ] [ gradient penalty ] [ gan ]
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.
Live content is unavailable. Log in and register to view live content