  Despite remarkable performance in producing realistic samples, Generative
Adversarial Networks (GANs) often produce low-quality samples near low-density
regions of the data manifold, e.g., samples of minor groups. Many techniques
have been developed to improve the quality of generated samples, either by
post-processing generated samples or by pre-processing the empirical data
distribution, but at the cost of reduced diversity. To promote diversity in
sample generation without degrading the overall quality, we propose a simple
yet effective method to diagnose and emphasize underrepresented samples during
training of a GAN. The main idea is to use the statistics of the discrepancy
between the data distribution and the model distribution at each data instance.
Based on the observation that the underrepresented samples have a high average
discrepancy or high variability in discrepancy, we propose a method to
emphasize those samples during training of a GAN. Our experimental results
demonstrate that the proposed method improves GAN performance on various
datasets, and it is especially effective in improving the quality and diversity
of sample generation for minor groups.
