Advances in generative adversarial network
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...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018032/ |
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| _version_ | 1841539406508326912 |
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| author | Wanliang WANG Zhuorong LI |
| author_facet | Wanliang WANG Zhuorong LI |
| author_sort | Wanliang WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-17cc3f6063054652beccc7f0fe35479c |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-17cc3f6063054652beccc7f0fe35479c2025-01-14T07:14:19ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-02-013913514859716701Advances in generative adversarial networkWanliang WANGZhuorong LIGenerative 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018032/deep learning,generative adversarial networkconvolutional neural networkauto-encoderadversarial training |
| spellingShingle | Wanliang WANG Zhuorong LI Advances in generative adversarial network Tongxin xuebao deep learning,generative adversarial network convolutional neural network auto-encoder adversarial training |
| title | Advances in generative adversarial network |
| title_full | Advances in generative adversarial network |
| title_fullStr | Advances in generative adversarial network |
| title_full_unstemmed | Advances in generative adversarial network |
| title_short | Advances in generative adversarial network |
| title_sort | advances in generative adversarial network |
| topic | deep learning,generative adversarial network convolutional neural network auto-encoder adversarial training |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018032/ |
| work_keys_str_mv | AT wanliangwang advancesingenerativeadversarialnetwork AT zhuorongli advancesingenerativeadversarialnetwork |