通信学报2018，Vol.39Issue(2)：135-148,14.DOI:10.11959/j.issn.1000-436x.2018032
生成式对抗网络研究进展
Advances in generative adversarial network
摘要
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 re-cent 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 re-quire urgent solutions and works that deserve further investigation were discussed.关键词
深度学习/生成式对抗网络/卷积神经网络/自动编码器/对抗训练Key words
deep learning/generative adversarial network/convolutional neural network/auto-encoder/adversarial training分类
信息技术与安全科学引用本文复制引用
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):135-148,14.基金项目
国家自然科学基金资助项目(No.61379123)Foundation Item: The National Natural Science Foundation of China (No.61379123) （No.61379123）
