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Cited by 267Generative Adversarial Networks and Image SynthesisDigital Media Forensic DetectionAdvanced Neural Network Applications

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

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Wang, Zhengwei, She, Qi, Ward, Tomás E.·2022·ACM Computing Surveys
DOI 10.1145/3439723
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Abstract

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation, and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are as follows: (1) the generation of high quality images, (2) diversity of image generation, and (3) stabilizing training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state-of-the-art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress toward addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Codes related to the GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.

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  • The generator in the GAN can generate synthetic data close to the real data, so adversarial representation learning has a wide range of applications in the data generation field.

    “The generator in the GAN can generate synthetic data close to the real data, so adversarial representation learning has a wide range of applications in the data generation field [149], especially in image generation [150]-[153].”

    From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [149]

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Authors
Wang, Z., She, Q., & Ward, T. E.
Affiliations
  • Trinity College Dublin (IE)
  • ByteDance (China) (CN)
  • Insight Centre for Data Analytics, School of Computing, Dublin City University, Glasnevin, Dublin, Ireland
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-02-09
Volume
54
Issue
2
Pages
1-38
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3439723
OpenAlex
W3127284993
Cited by
267
References
92
Citations by year
2026: 14 · 2025: 52 · 2024: 70 · 2023: 41 · 2022: 40 · 2021: 46 · 2020: 4
FWCI
17.73
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Computing Surveys (publishedVersion)
Funders
Science Foundation Ireland (SFI/12/RC/2289_P2)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
generative adversarial networks, image generation, image-to-image translation, GAN training stability, GAN architectures, loss functions

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