NSSC OpenIR
Research Progress on Generative Adversarial Network with its Applications
Alternative Title20203809211498
Zhang, Zhongwei
Source PublicationProceedings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020
2020
Pages9141685
DOI10.1109/ITOEC49072.2020.9141685
Language英语
ISBN9781728143224
AbstractAs a new unsupervised learning algorithm framework, generative adversarial networks(GAN) have been favored by more and more researchers, and it has become a research hotspot now. GAN is inspired by the two-person zero-sum game theory in game theory. Its unique adversarial training idea can generate high-quality samples and has more powerful feature learning and feature expression capabilities than traditional machine learning algorithms. At present, GAN has achieved remarkable success in the field of computer vision, especially in the field of sample generation. Every year, a large number of GAN-related research papers are produced, reflecting the fiery degree of research on GAN model. Aiming at the hot model of GAN, first introduce the research status of GAN; then introduce the theory and framework of GAN, which analyzes in detail why the gradient disappears and the mode collapses during the training of GAN; then discussed some typical GAN improvement models, and summarized their theoretical improvements, advantages, limitations, application scenarios and implementation costs; Finally, the application results of GAN in data generation, image super-resolution, and image style conversion are shown, and the current challenges and future research directions of GAN are discussed. © 2020 IEEE.
KeywordAdversarial networks Application scenario Feature expression Future research directions Image super resolutions Implementation cost Sample generations Two-person zero-sum game
Conference Name5th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2020
Conference DateJune 12, 2020 - June 14, 2020
Conference PlaceChongqing, China
Indexed ByEI
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Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7745
Collection中国科学院国家空间科学中心
Corresponding AuthorZhang, Zhongwei
AffiliationNational Space Science Center, Chinese Academy of Sciences NO. 1 Nanertiao, Zhongguancun, Haidian District, Beijing; 100190, China
Recommended Citation
GB/T 7714
Zhang, Zhongwei. Research Progress on Generative Adversarial Network with its Applications[C],2020:9141685.
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