Attention Guided Multiple Source and Target Domain Adaptation
文献类型：期刊论文
| 作者 | Wang, Yuxi1,2 ; Zhang, Zhaoxiang1,2; Hao, Wangli1,2; Song, Chunfeng1,2
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2021 |
| 卷号 | 30页码:892-906 |
| 关键词 | Semantics Task analysis Generators Generative adversarial networks Feature extraction Visualization Meteorology Domain adaptation multiple source and target domains attention |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2020.3031161 |
| 通讯作者 | Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn) |
| 英文摘要 | Domain adaptation aims to alleviate the distribution discrepancy between source and target domains. Most conventional methods focus on one target domain setting adapted from one or multiple source domains while neglecting the multi-target domain setting. We argue that different target domains also have complementary information, which is very important for performance improvement. In this paper, we propose an Attention-guided Multiple source-and-target Domain Adaptation (AMDA) method to capture the context dependency information on transferable regions among multiple source and target domains. The innovation points of this paper are as follows: (1) We use numerous adversarial strategies to harvest sufficient information from multiple source and target domains, which extends the generalization and robustness of the feature pools. (2) We propose an intra-domain and inter-domain attention module to explore transferable context information. The proposed attention module can learn domain-invariant representations and reduce the negative transfer by focusing on transferable knowledge. Extensive experiments validate the effectiveness of our method with achieving state-of-the-art performance on several unsupervised domain adaptation datasets. |
| 资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[61761146004] ; National Natural Science Foundation of China[61773375] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000597146900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42673]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhang, Zhaoxiang |
| 作者单位 | 1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yuxi,Zhang, Zhaoxiang,Hao, Wangli,et al. Attention Guided Multiple Source and Target Domain Adaptation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:892-906. |
| APA | Wang, Yuxi,Zhang, Zhaoxiang,Hao, Wangli,&Song, Chunfeng.(2021).Attention Guided Multiple Source and Target Domain Adaptation.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,892-906. |
| MLA | Wang, Yuxi,et al."Attention Guided Multiple Source and Target Domain Adaptation".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):892-906. |
入库方式： OAI收割
来源：自动化研究所
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