Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments
文献类型：期刊论文
| 作者 | Zhuo, Junbao3; Wang, Shuhui2,3; Huang, Qingming1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2023 |
| 卷号 | 25页码:6157-6170 |
| 关键词 | Domain Adaptation Uncertainty Noisy Label Transfer Learning Deep Learning |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2022.3205457 |
| 英文摘要 | In this paper, we tackle the task of domain adaptation under noisy environments; this is a practical and challenging problem in which the source domain is corrupted with noise in its labels, its features, or both. Noise in the source domain leads to inaccurate visual representations and makes it harder to estimate and reduce the domain discrepancy between the source and target domains, resulting in severe performance degradation in the target domain. These challenges can be addressed with offline source sample selection following robust domain discrepancy reduction. To achieve reliable sample selection, we model the uncertainty in the predictions of a convolutional neural network (CNN) classifier and reweight the classification loss by this uncertainty. Such a reweighting mechanism reduces the contribution of noise, leading to improved noise robustness. We further propose UncertaintyRank, a novel regularizer, to encourage the uncertainty to be more sensitive to noisy labels, as label corruption brings more severe degradation. The uncertainty is also aggregated with the classification loss to eliminate the adverse effects of noisy representations while estimating the domain discrepancy. Extensive experiments validate the effectiveness of our method and verify that it performs favorably against existing state-of-the-art methods. |
| 资助项目 | National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation of China[62022083] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[61931008] ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001098831500037 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38069]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Shuhui |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 2.Peng Cheng Lab, Shenzhen 518066, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhuo, Junbao,Wang, Shuhui,Huang, Qingming. Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:6157-6170. |
| APA | Zhuo, Junbao,Wang, Shuhui,&Huang, Qingming.(2023).Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments.IEEE TRANSACTIONS ON MULTIMEDIA,25,6157-6170. |
| MLA | Zhuo, Junbao,et al."Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):6157-6170. |
入库方式： OAI收割
来源：计算技术研究所
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