  In this paper, we present a simple approach to train Generative Adversarial
Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit
models such as GANs tend to generate better samples compared to explicit models
that are trained on tractable data likelihood. However, GANs overlook the
explicit data density characteristics which leads to undesirable quantitative
evaluations and mode collapse. To bridge this gap, we propose a hybrid
generative adversarial network (HGAN) for which we can enforce data density
estimation via an autoregressive model and support both adversarial and
likelihood framework in a joint training manner which diversify the estimated
density in order to cover different modes. We propose to use an adversarial
network to \textit {transfer knowledge} from an autoregressive model (teacher)
to the generator (student) of a GAN model. A novel deep architecture within the
GAN formulation is developed to adversarially distill the autoregressive model
information in addition to simple GAN training approach. We conduct extensive
experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to
demonstrate the effectiveness of the proposed HGAN under qualitative and
quantitative evaluations. The experimental results show the superiority and
competitiveness of our method compared to the baselines.
