Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (10): 1795-1811.DOI: 10.3778/j.issn.1673-9418.2103075

• Surveys and Frontiers • Previous Articles     Next Articles

Generative Adversarial Network and Its Research Progress in Image Generation

MA Yongjie, XU Xiaodong, ZHANG Ru, XIE Yirong, CHEN Hong   

  1. School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
  • Online:2021-10-01 Published:2021-09-30



  1. 西北师范大学 物理与电子工程学院,兰州 730070


Generative adversarial networks (GAN) has become a popular research direction in the field of deep learning. The unique adversarial idea of GAN comes from the two-player zero-sum game in game theory. How to solve the problems of unstable GAN training, poor quality of generated samples, inadequate evaluation system, poor interpretability and other issues is critical and difficult in current GAN research. This paper investigates the research background and development trend of GAN. First, the basic idea and algorithm implementation of GAN are described, the advantages and disadvantages of GAN are analyzed, a more systematic classification of existing improved methods is made, and some typical optimization methods and derivative models of GAN are sorted out from two categories based on structure change and loss function variants respectively. Next, the similarities and differences between GAN and other generative models are compared, and their respective advantages and disadvantages are introduced. Then, this paper compares the performance of GAN and its derivative models, and summarizes their operation mechanism, advantages, limitations and applicable scenarios. The applications of GAN in the field of image generation are introduced. Finally, the mainstream evaluation indicators of GAN are listed, the main problems still faced in GAN research are analyzed and corresponding solutions are given. The listed mainstream solutions are compared and analyzed in terms of solution effects and applicability, and the future research direction is prospected.

Key words: generative adversarial networks (GAN), machine learning, deep learning, image processing, unsupervised learning, image generation



关键词: 生成式对抗网络(GAN), 机器学习, 深度学习, 图像处理, 无监督学习, 图像生成