生成对抗网络GAN综述

计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 74-81.doi: 10.11896/j.issn.1002-137X.2019.03.009

生成对抗网络GAN综述

程显毅^1,2,谢璐²,朱建新^2,3,胡彬²,施佺²

(硅湖职业技术学院江苏昆山 215300)¹
(南通先进通信技术研究院(南通大学) 江苏南通 226019)²
(武汉理工大学信息工程学院武汉 430010)³

收稿日期:2018-02-12 修回日期:2018-06-09 出版日期:2019-03-15 发布日期:2019-03-22
通讯作者: 程显毅(1956-),男,教授,主要研究方向为机器学习、自然语言处理,E-mail:xycheng@ntu.edu.cn
作者简介:谢璐(1990-),女,硕士生,主要研究方向为深度学习;朱建新((1976-),男,博士生,副教授,主要研究方向为大数据技术;胡彬(1980-),男,博士,主要研究方向为图像处理;施佺(1973-),男,教授,主要研究方向为智能信息处理。
基金资助:
国家自然科学基金项目(61771265,61340037),江苏省现代教育技术研究课题(2017-R-54131),南通大学-南通智能信息技术联合研究中心开放课题(KFKT2016B06)资助

Review of Generative Adversarial Network

CHENG Xian-yi^1,2,XIE Lu²,ZHU Jian-xin^2,3,HU Bin²,SHI Quan²

(Silicon Lake College,Kunshan,Jiangsu 215300,China)¹
(Nantong Research Institute for Advanced Communication Technologies(Nantong University),Nantong,Jiangsu 226019,China)²
(School of Information Engineering,Wuhan University of Technology,Wuhan 430010,China)³

Received:2018-02-12 Revised:2018-06-09 Online:2019-03-15 Published:2019-03-22

摘要/Abstract

摘要： 人能够理解事物运动的方式,因此对事物未来发展的预测比机器准。不过,作为一种新的深度神经网络系统,GAN(Generative Adversarial Network)生成的数据非常逼真,连人也无法辨别数据是真实的还是生成的。从某种意义上讲,GAN为指导人工智能系统完成复杂任务提供了一种全新的思路,让机器成为了一个专家。首先,讨论了GAN的基本模型和一些改进的GAN模型;然后,展示了GAN在超分辨图像生成、由文本描述生成图像、艺术风格图像生成和短视频生成方面的应用成果;最后,探讨了GAN在理论、架构和应用方面所面临的问题和其未来的研究方向。

关键词: 判别器, 人工智能, 深度学习, 生成对抗网络, 生成器

Abstract: Humans can understand the way of movement,so they can predictthe future development of things more accurately than machines.But GAN (Generative Adversarial Network) is a new neural Network system,its dataare very lifelike,even people can’t identify whether the data are real or generated.In a sense,GAN provides a brand new thought for guiding the artificial intelligence system to accomplish complex tasks,and makes the machine a specialist.In this paper,first of all,the basic model and some improvements model of GAN were discussed.Then,some application achievements of GAN were shown,such as the images generated by the super resolution,by a text description,by the artistic style and short video generated.Finally,some problems of theory,architecture,and application in the future research were discussed

Key words: Artificial intelligence, Deep learning, Discriminator, GAN, Generator

中图分类号:

TP181

程显毅,谢璐,朱建新,胡彬,施佺. 生成对抗网络GAN综述[J]. 计算机科学, 2019, 46(3): 74-81. https://doi.org/10.11896/j.issn.1002-137X.2019.03.009

CHENG Xian-yi,XIE Lu,ZHU Jian-xin,HU Bin,SHI Quan. Review of Generative Adversarial Network[J]. Computer Science, 2019, 46(3): 74-81. https://doi.org/10.11896/j.issn.1002-137X.2019.03.009

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