HGAN: Hybrid generative adversarial network

Article type: Research Article

Authors: Iranmanesh, Seyed Mehdi^{; *} | Nasrabadi, Nasser M.

Affiliations: West Virginia University, Morgantown, WV, USA

Correspondence: [*] Corresponding author. Seyed Mehdi Iranmanesh, Ph.D., Morgantown, WV, USA. E-mail: seiranmanesh@mix.wvu.edu.

Abstract: In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a 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 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.

Keywords: Generative adversarial network, adversarial training, mode collapse, network distillation, autoregressive model

DOI: 10.3233/JIFS-201202

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8927-8938, 2021