A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Abstract

Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. However, few comprehensive studies exist explaining the connections among different GANs variants and how they have evolved. In this paper, we attempt to provide a review of the various GANs methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GANs algorithms are introduced in detail and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are discussed.

Keywords

Affiliated Institutions

• University of Michigan US
• Jingdong (China) CN
• Chinese Academy of Sciences CN
• The University of Sydney AU
• Purple Mountain Laboratories CN
• Southeast University CN
• Nanyang Technological University SG

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