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JACIII Vol.26 No.1 pp. 3-7
doi: 10.20965/jaciii.2022.p0003
(2022)

Review:

Review of Generative Adversarial Networks in Image Generation

Wanle Chi*,**,†, Yun Huoy Choo**, and Ong Sing Goh**

*Department of Information Technology, Wenzhou Polytechnic
University Town, Chashan, Wenzhou, Zhejiang 325035, China

**Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM)
Malacca 76100, Malaysia

Corresponding author

Received:
May 31, 2020
Accepted:
March 11, 2021
Published:
January 20, 2022
Keywords:
image generation, generative adversarial networks, machine learning, gradients disappearing, collapse mode
Abstract
Review of Generative Adversarial Networks in Image Generation

Review of GANs in image generation

Generative adversarial network (GAN) model generates and discriminates images using an adversarial competitive strategy to generate high-quality images. The implementation of GAN in different fields is helpful for generating samples that are not easy to obtain. Image generation can help machine learning to balance data and improve the accuracy of the classifier. This paper introduces the principles of the GAN model and analyzes the advantages and disadvantages of improving GANs. The applications of GANs in image generation are analyzed. Finally, the problems of GANs in image generation are summarized.

Cite this article as:
W. Chi, Y. Choo, and O. Goh, “Review of Generative Adversarial Networks in Image Generation,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.1, pp. 3-7, 2022.
Data files:
References
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Last updated on Aug. 05, 2022