  Despite remarkable performance in producing realistic samples, Generative
Adversarial Networks (GANs) often produce low-quality samples near low-density
regions of the data manifold. Recently, many techniques have been developed to
improve the quality of generated samples, either by rejecting low-quality
samples after training or by pre-processing the empirical data distribution
before training, but at the cost of reduced diversity. To guarantee both the
quality and the diversity, 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 of generated samples with minor features.
