Review of Generative Adversarial Networks in Image Generation

被引:6
|
作者
Chi, Wanle [1 ,2 ]
Choo, Yun Huoy [2 ]
Goh, Ong Sing [2 ]
机构
[1] Wenzhou Polytech, Dept Informat Technol, Wenzhou 325035, Zhejiang, Peoples R China
[2] Univ Tekn Malaysia Melaka UTeM, Fac Informat & Commun Technol, Malacca 76100, Malaysia
关键词
image generation; generative adversarial networks; machine learning; gradients disappearing; collapse mode;
D O I
10.20965/jaciii.2022.p0003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:3 / 7
页数:5
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