A Review of Application of Generative Adversarial Networks

被引：0
|
作者：
Ye C. [1 ]
Guan W. [1 ]
机构：
[1] Key Laboratory of Embedded System and Service Computing of the Ministry of Education, Tongji University, Shanghai
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2020年 / 48卷 / 04期
关键词：
Conditional generative model; Generative adversarial networks(GAN); Image generation;
D O I：
10.11908/j.issn.0253-374x.19204
中图分类号：
学科分类号：
摘要：
Generative adversarial networks (GAN) is an excellent generative model, which can learn high-dimensional and complex real data distribution without relying on any prior assumptions. This powerful performance makes it a research hotspot in recent years, and remarkable progress has been made in research in many application fields. In this paper, the basic principle of the GAN, various objective functions and common model structures are introduced. Then, the evolutional methods for generating images under the constraints of conditional generative adversarial networks are analyzed in detail. After that, the applications of the GAN in different fields are introduced, including high-resolution image generation, small target detection, non-image data generation, medical image segmentation and so on. Finally, the optimization techniques in the training process of the GAN are summarized. The purpose of this paper is to elucidate the basic theory and development history of GAN, and to forcast the future work from the perspective of application. © 2020, Editorial Department of Journal of Tongji University. All right reserved.
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页码：591 / 601
页数：10
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