volume 40, issue 5, P8927-8938 2021
DOI: 10.3233/jifs-201202
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Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi

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 de…

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