  The two key players in Generative Adversarial Networks (GANs), the
discriminator and generator, are usually parameterized as deep neural networks
(DNNs). On many generative tasks, GANs achieve state-of-the-art performance but
are often unstable to train and sometimes miss modes. A typical failure mode is
the collapse of the generator to a single parameter configuration where its
outputs are identical. When this collapse occurs, the gradient of the
discriminator may point in similar directions for many similar points. We
hypothesize that some of these shortcomings are in part due to primitive and
redundant features extracted by discriminator and this can easily make the
training stuck. We present a novel approach for regularizing adversarial models
by enforcing diverse feature learning. In order to do this, both generator and
discriminator are regularized by penalizing both negatively and positively
correlated features according to their differentiation and based on their
relative cosine distances. In addition to the gradient information from the
adversarial loss made available by the discriminator, diversity regularization
also ensures that a more stable gradient is provided to update both the
generator and discriminator. Results indicate our regularizer enforces diverse
features, stabilizes training, and improves image synthesis.
