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 DCGAN in Different Application Fields

Authors
Chongyue Liu1, *
1School of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, Jiangsu, 211816, China
*Corresponding author. Email: 202321168012@njtech.edu.cn
Corresponding Author
Chongyue Liu
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_94How to use a DOI?
Keywords
Deep Convolution Generative Adversarial Networks; Convolutional Neural Network; Image Generation
Abstract

Deep convolutional Generative Adversarial Network (DCGAN) is an important type of generative adversarial network. It integrates convolutional neural network (CNN) into the adversarial framework and performs well in image generation and data augmentation. This paper reviews and summarizes the basic technical points of DCGAN, including its network structure and key optimization methods. The application fields of DCGAN are mainly introduced: industrial defect detection (bearings, fabrics), agricultural pesticide residue detection and plant disease identification, as well as medical diagnostic image synthesis. Its value lies in solving the problems of small samples and the imbalance of datasets. DCGAN is increasingly used in these application fields due to its outstanding performance. Despite the challenges such as unstable training, model collapse and ethical risks that DCGAN faces, it still has a promising future. In the future, DCGAN will break data bottlenecks, drive industries to shift from experience-driven to data-driven, and become a core engine for multi-field intelligent upgrading.

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_94How 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  - Chongyue Liu
PY  - 2026
DA  - 2026/04/24
TI  - Research and Analysis of DCGAN in Different Application Fields
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 870
EP  - 878
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_94
DO  - 10.2991/978-94-6239-648-7_94
ID  - Liu2026
ER  -