  Building on the success of deep learning, Generative Adversarial Networks
(GANs) provide a modern approach to learn a probability distribution from
observed samples. GANs are often formulated as a zero-sum game between two sets
of functions; the generator and the discriminator. Although GANs have shown
great potentials in learning complex distributions such as images, they often
suffer from the mode collapse issue where the generator fails to capture all
existing modes of the input distribution. As a consequence, the diversity of
generated samples is lower than that of the observed ones. To tackle this
issue, we take an information-theoretic approach and maximize a variational
lower bound on the entropy of the generated samples to increase their
diversity. We call this approach GANs with Variational Entropy Regularizers
(GAN+VER). Existing remedies for the mode collapse issue in GANs can be easily
coupled with our proposed variational entropy regularization. Through extensive
experimentation on standard benchmark datasets, we show all the existing
evaluation metrics highlighting difference of real and generated samples are
significantly improved with GAN+VER.
