Mode Regularized Generative Adversarial Networks

TL;DR

This paper introduces regularization techniques for GANs that stabilize training and improve mode coverage, addressing common issues like instability and mode collapse.

Contribution

The paper proposes novel regularizers that enhance GAN training stability and promote fair distribution of probability mass across data modes.

Findings

Regularizers significantly stabilize GAN training.

Improved mode coverage during early training phases.

Enhanced distributional fairness across data modes.

Abstract

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.