  We propose a novel regularizer to improve the training of Generative
Adversarial Networks (GANs). The motivation is that when the discriminator D
spreads out its model capacity in the right way, the learning signals given to
the generator G are more informative and diverse. These in turn help G to
explore better and discover the real data manifold while avoiding large
unstable jumps due to the erroneous extrapolation made by D. Our regularizer
guides the rectifier discriminator D to better allocate its model capacity, by
encouraging the binary activation patterns on selected internal layers of D to
have a high joint entropy. Experimental results on both synthetic data and real
datasets demonstrate improvements in stability and convergence speed of the GAN
training, as well as higher sample quality. The approach also leads to higher
classification accuracies in semi-supervised learning.
