  Recent years have witnessed the rapid progress of generative adversarial
networks (GANs). However, the success of the GAN models hinges on a large
amount of training data. This work proposes a regularization approach for
training robust GAN models on limited data. We theoretically show a connection
between the regularized loss and an f-divergence called LeCam-divergence, which
we find is more robust under limited training data. Extensive experiments on
several benchmark datasets demonstrate that the proposed regularization scheme
1) improves the generalization performance and stabilizes the learning dynamics
of GAN models under limited training data, and 2) complements the recent data
augmentation methods. These properties facilitate training GAN models to
achieve state-of-the-art performance when only limited training data of the
ImageNet benchmark is available.
