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Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection

文献类型：期刊论文

作者Yu, Zhipeng2; Xu, Qianqian1; Jiang, Yangbangyan3; Sun, Yingfei2; Huang, Qingming1,3
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2024
卷号33页码:6083-6097
关键词Metric learning noisy label deep learning deep learning pair- wise loss pair- wise loss pair- wise loss pair- wise loss positive-pair selection positive-pair selection
ISSN号1057-7149
DOI10.1109/TIP.2024.3482182
英文摘要The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods.
资助项目National Key Research and Development Program of China[2018AAA0102000] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[U23B2051] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[62122075] ; National Natural Science Foundation of China[62406305] ; National Natural Science Foundation of China[62471013] ; National Natural Science Foundation of China[62476068] ; National Natural Science Foundation of China[62272439] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0680000] ; Innovation Funding of Institute of Computing Technology (ICT), CAS[E000000] ; China Postdoctoral Science Foundation (CPSF)[2023M743441] ; Postdoctoral Fellowship Program of CPSF[GZB20230732]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001342519900005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39459]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jiang, Yangbangyan; Sun, Yingfei
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Zhipeng,Xu, Qianqian,Jiang, Yangbangyan,et al. Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:6083-6097.
APA Yu, Zhipeng,Xu, Qianqian,Jiang, Yangbangyan,Sun, Yingfei,&Huang, Qingming.(2024).Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,6083-6097.
MLA Yu, Zhipeng,et al."Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):6083-6097.

入库方式： OAI收割

来源：计算技术研究所

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