  We propose a new algorithm to incorporate class conditional information into
the critic of GANs via a multi-class generalization of the commonly used Hinge
loss that is compatible with both supervised and semi-supervised settings. We
study the compromise between training a state of the art generator and an
accurate classifier simultaneously, and propose a way to use our algorithm to
measure the degree to which a generator and critic are class conditional. We
show the trade-off between a generator-critic pair respecting class
conditioning inputs and generating the highest quality images. With our
multi-hinge loss modification we are able to improve Inception Scores and
Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code
available at https://github.com/ilyakava/gan.
