Title: Fisher GAN,
Abstract: Generative Adversarial Networks (GANs) are powerful models for learning
complex distributions. Stable training of GANs has been addressed in many
recent works which explore different metrics between distributions. In this
paper we introduce Fisher GAN which fits within the Integral Probability
Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a
data dependent constraint on its second order moments. We show in this paper
that Fisher GAN allows for stable and time efficient training that does not
compromise the capacity of the critic, and does not need data independent
constraints such as weight clipping. We analyze our Fisher IPM theoretically
and provide an algorithm based on Augmented Lagrangian for Fisher GAN. We
validate our claims on both image sample generation and semi-supervised
classification using Fisher GAN.