  Generative Adversarial Networks (GANs) have brought about rapid progress
towards generating photorealistic images. Yet the equitable allocation of their
modeling capacity among subgroups has received less attention, which could lead
to potential biases against underrepresented minorities if left uncontrolled.
In this work, we first formalize the problem of minority inclusion as one of
data coverage, and then propose to improve data coverage by harmonizing
adversarial training with reconstructive generation. The experiments show that
our method outperforms the existing state-of-the-art methods in terms of data
coverage on both seen and unseen data. We develop an extension that allows
explicit control over the minority subgroups that the model should ensure to
include, and validate its effectiveness at little compromise from the overall
performance on the entire dataset. Code, models, and supplemental videos are
available at GitHub.
