Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Research and Analysis of Generative Adversarial Networks in the Field of Computer Vision

Authors
Longxuan Li1, *
1School of Electronic Information, Shanghai Dianji University, 300 Shuihua Road Pudong New District, Shanghai, China
*Corresponding author. Email: Longxuanli808@gmail.com
Corresponding Author
Longxuan Li
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_95How to use a DOI?
Keywords
GAN; Computer vision; Vertical application areas
Abstract

The integration of Generative Adversarial Networks (GANs) with various domains in computer vision has become one of the key topics in current research. Researchers have found that GANs have outperformed existing models in vertical applications, significantly enhancing and expanding model training performance and practical requirements across different fields. However, there is still a lack of a unified understanding regarding the systematic summarization and key points of GANs in various vertical domains. Therefore, this paper aims to provide a systematic review of the innovative research progress and breakthroughs made by GANs in the fields of medicine, biomolecules, agriculture, and remote sensing. By detailing the research processes and experimental results of GANs in these four application areas, the paper clarifies their core value and role in each field and establishes that future research in this direction is gradually moving toward goals of lightweight models, high generalization, and wide applicability. This review aims to give researchers a clearer understanding of the applications and impact of GANs across different domains, thereby laying a foundation for their use in even broader application areas in the future.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_95How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Longxuan Li
PY  - 2026
DA  - 2026/04/24
TI  - Research and Analysis of Generative Adversarial Networks in the Field of Computer Vision
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 879
EP  - 892
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_95
DO  - 10.2991/978-94-6239-648-7_95
ID  - Li2026
ER  -