Inclusive GAN: Improving Data and Minority Coverage in Generative Models
- URL: http://arxiv.org/abs/2004.03355v3
- Date: Sun, 23 Aug 2020 01:10:45 GMT
- Title: Inclusive GAN: Improving Data and Minority Coverage in Generative Models
- Authors: Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, Mario Fritz
- Abstract summary: We formalize the problem of minority inclusion as one of data coverage.
We then propose to improve data coverage by harmonizing adversarial training with reconstructive generation.
We develop an extension that allows explicit control over the minority subgroups that the model should ensure to include.
- Score: 101.67587566218928
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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