Prototype learning for adversarial domain adaptation
文献类型：期刊论文
| 作者 | Fang, Yuchun1; Chen, Chen1 ; Zhang, Wei1; Wu, Jiahua1; Zhang, Zhaoxiang2; Xie, Shaorong1
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 155页码:12 |
| 关键词 | Domain adaptation Transfer learning Unsupervised learning Deep learning |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2024.110653 |
| 通讯作者 | Fang, Yuchun(ycfang@shu.edu.cn) |
| 英文摘要 | Adversarial learning has been widely used in recent years to address the issue of domain shift in domain adaptation. However, this approach focuses on global cross -domain alignment and overlooks the alignment of class boundaries. To tackle this limitation, we introduce a novel method called PLADA. PLADA leverages prototype learning to align category distributions across domains. The prototypes in PLADA represent the source category distribution, which is constructed using labelled data and transferred to the target domain. In addition to adversarial learning for global domain -invariant feature learning, we propose the weighted prototype loss (WPL) to embed prototype information. WPL transforms the local category distribution alignment problem into a distance measurement between the prediction and prototypes, resulting in a more discriminative representation. Experimental results demonstrate that our proposed model performs comparably well on multiple classic domain adaptation tasks, showcasing the potential of PLADA. |
| 资助项目 | National Natural Science Foundation of China[61976132] ; National Natural Science Foundation of China[61991411] ; National Natural Science Foundation of China[U1811461] ; Natural Science Foundation of Shanghai, China[19ZR1419200] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001263808600001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shanghai, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59222]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Fang, Yuchun |
| 作者单位 | 1.Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Yuchun,Chen, Chen,Zhang, Wei,et al. Prototype learning for adversarial domain adaptation[J]. PATTERN RECOGNITION,2024,155:12. |
| APA | Fang, Yuchun,Chen, Chen,Zhang, Wei,Wu, Jiahua,Zhang, Zhaoxiang,&Xie, Shaorong.(2024).Prototype learning for adversarial domain adaptation.PATTERN RECOGNITION,155,12. |
| MLA | Fang, Yuchun,et al."Prototype learning for adversarial domain adaptation".PATTERN RECOGNITION 155(2024):12. |
入库方式： OAI收割
来源：自动化研究所
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