Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation
文献类型：期刊论文
| 作者 | Zefeng Zheng; Luyao Teng; Wei Zhang ; Naiqi Wu; Shaohua Teng
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| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:12页码:2269-2291 |
| ISSN号 | 2329-9266 |
| 关键词 | Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation |
| DOI | 10.1109/JAS.2023.123342 |
| 英文摘要 | Most existing domain adaptation (DA) methods aim to explore favorable performance under complicated environments by sampling. However, there are three unsolved problems that limit their efficiencies: i) they adopt global sampling but neglect to exploit global and local sampling simultaneously; ii) they either transfer knowledge from a global perspective or a local perspective, while overlooking transmission of confident knowledge from both perspectives; and iii) they apply repeated sampling during iteration, which takes a lot of time. To address these problems, knowledge transfer learning via dual density sampling (KTL-DDS) is proposed in this study, which consists of three parts: i) Dual density sampling (DDS) that jointly leverages two sampling methods associated with different views, i.e., global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information; ii) Consistent maximum mean discrepancy (CMMD) that reduces intra- and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS; and iii) Knowledge dissemination (KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain. Mathematical analyses show that DDS avoids repeated sampling during the iteration. With the above three actions, confident knowledge with both global and local properties is transferred, and the memory and running time are greatly reduced. In addition, a general framework named dual density sampling approximation (DDSA) is extended, which can be easily applied to other DA algorithms. Extensive experiments on five datasets in clean, label corruption (LC), feature missing (FM), and LC&FM environments demonstrate the encouraging performance of KTL-DDS. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52470]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Zefeng Zheng,Luyao Teng,Wei Zhang,et al. Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(12):2269-2291. |
| APA | Zefeng Zheng,Luyao Teng,Wei Zhang,Naiqi Wu,&Shaohua Teng.(2023).Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation.IEEE/CAA Journal of Automatica Sinica,10(12),2269-2291. |
| MLA | Zefeng Zheng,et al."Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation".IEEE/CAA Journal of Automatica Sinica 10.12(2023):2269-2291. |
入库方式： OAI收割
来源：自动化研究所
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