Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162749
Title: A review on generative adversarial networks: algorithms, theory, and applications
Authors: Gui, Jie
Sun, Zhenan
Wen, Yonggang
Tao, Dacheng
Ye, Jieping
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Gui, J., Sun, Z., Wen, Y., Tao, D. & Ye, J. (2021). A review on generative adversarial networks: algorithms, theory, and applications. IEEE Transactions On Knowledge and Data Engineering, 1-20. https://dx.doi.org/10.1109/TKDE.2021.3130191
Journal: IEEE Transactions on Knowledge and Data Engineering
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.
URI: https://hdl.handle.net/10356/162749
ISSN: 1041-4347
DOI: 10.1109/TKDE.2021.3130191
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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