Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration

Zhenyu Wu, Ye Yuan, Zhaowen Wang, Jianming Zhang, Zhangyang Wang, Hailin Jin

Abstract: Generative adversarial networks (GANs) nowadays are capable of producing im-ages of incredible realism. One concern raised is whether the state-of-the-artGAN’s learned distribution still suffers from mode collapse. Existing evaluation metrics for image synthesis focus on low-level perceptual quality. Diversity tests of samples from GANs are usually conducted qualitatively on a small scale. In this work, we devise a set of statistical tools, that are broadly applicable to quantitatively measuring the mode collapse of GANs. Strikingly, we consistently observe strong mode collapse on several state-of-the-art GANs using our toolset. We analyze possible causes, and for the first time present two simple yet effective “black-box” methods to calibrate the GAN learned distribution, without accessing either model parameters or the original training data.

Keywords: Generative Adversarial Networks, Mode Collapse, Calibration