  Generative adversarial nets (GANs) are a promising technique for modeling a
distribution from samples. It is however well known that GAN training suffers
from instability due to the nature of its maximin formulation. In this paper,
we explore ways to tackle the instability problem by dualizing the
discriminator. We start from linear discriminators in which case conjugate
duality provides a mechanism to reformulate the saddle point objective into a
maximization problem, such that both the generator and the discriminator of
this 'dualing GAN' act in concert. We then demonstrate how to extend this
intuition to non-linear formulations. For GANs with linear discriminators our
approach is able to remove the instability in training, while for GANs with
nonlinear discriminators our approach provides an alternative to the commonly
used GAN training algorithm.
