生成式对抗网络研究进展

王万良; 李卓蓉

doi:10.11959/j.issn.1000-436x.2018032

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生成式对抗网络研究进展

综述 | 更新时间：2024-06-05

- 生成式对抗网络研究进展
- Advances in generative adversarial network
- 通信学报 2018年39卷第2期页码：135-148
- 作者机构：
  
  浙江工业大学计算机科学与技术学院，浙江杭州 310024
- 作者简介：
  
  [ "王万良（1957-），男，江苏高邮人，博士，浙江工业大学教授，主要研究方向为人工智能、机器自动化、网络控制。" ]
  [ "李卓蓉（1986-），女，广西桂林人，浙江工业大学博士生，主要研究方向为人工智能、深度学习。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61379123)
- DOI：10.11959/j.issn.1000-436x.2018032
  中图分类号： TP183
- 网络出版日期：2018-02，
  
  纸质出版日期：2018-02-25
- 稿件说明：
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王万良, 李卓蓉. 生成式对抗网络研究进展[J]. 通信学报, 2018,39(2):135-148.

Wanliang WANG, Zhuorong LI. Advances in generative adversarial network[J]. Journal on communications, 2018, 39(2): 135-148.
王万良, 李卓蓉. 生成式对抗网络研究进展[J]. 通信学报, 2018,39(2):135-148. DOI： 10.11959/j.issn.1000-436x.2018032.

Wanliang WANG, Zhuorong LI. Advances in generative adversarial network[J]. Journal on communications, 2018, 39(2): 135-148. DOI： 10.11959/j.issn.1000-436x.2018032.

摘要

生成式对抗网络（GAN

generative adversarial network）对生成式模型的发展具有深远意义，自提出后立刻受到人工智能学术界和工业界的广泛研究与高度关注，随着深度学习的技术发展，生成式对抗模型在理论和应用上得到不断推进。首先，阐述生成对抗模型的研究背景与意义，然后，详细论述生成式对抗网络在建模、架构、训练和性能评估方面的研究进展及其具体应用现状，最后，进行分析与总结，指出生成式对抗网络研究中亟待解决的问题以及未来的研究方向。

Abstract

Generative adversarial network (GAN) have swiftly become the focus of considerable research in generative models soon after its emergence

whose academic research and industry applications have yielded a stream of further progress along with the remarkable achievements of deep learning.A broad survey of the recent advances in generative adversarial network was provided.Firstly

the research background and motivation of GAN was introduced.Then the recent theoretical advances of GAN on modeling

architectures

training and evaluation metrics were reviewed.Its state-of-the-art applications and the extensively used open source tools for GAN were introduced.Finally

issues that require urgent solutions and works that deserve further investigation were discussed.

关键词

Keywords

references

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