Towards prior gap and representation gap for long-tailed recognition
文献类型：期刊论文
| 作者 | Zhang, Ming-Liang1,2; Zhang, Xu-Yao1,2 ; Wang, Chuang1,2; Liu, Cheng-Lin1,2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2023 |
| 卷号 | 133页码:12 |
| ISSN号 | 0031-3203 |
| 关键词 | Long-tailed learning Prior gap Representation gap Image recognition |
| DOI | 10.1016/j.patcog.2022.109012 |
| 通讯作者 | Zhang, Ming-Liang(zhangmingliang2018@ia.ac.cn) |
| 英文摘要 | Most deep learning models are elaborately designed for balanced datasets, and thus they inevitably suf-fer performance degradation in practical long-tailed recognition tasks, especially to the minority classes. There are two crucial issues in learning from imbalanced datasets: skew decision boundary and unrep-resentative feature space. In this work, we establish a theoretical framework to analyze the sources of these two issues from Bayesian perspective, and find that they are closely related to the prior gap and the representation gap, respectively. Under this framework, we show that existing long-tailed recognition methods manage to remove either the prior gap or the presentation gap. Different from these methods, we propose to simultaneously remove the two gaps to achieve more accurate long-tailed recognition. Specifically, we propose the prior calibration strategy to remove the prior gap and introduce three strate-gies (representative feature extraction, optimization strategy adjustment and effective sample modeling) to mitigate the representation gap. Extensive experiments on five benchmark datasets validate the supe-riority of our method against the state-of-the-art competitors.(c) 2022 Elsevier Ltd. All rights reserved. |
| WOS关键词 | NEURAL-NETWORK CLASSIFICATION |
| 资助项目 | National Key Research and Development Program[2018AAA010 040 0] ; National Natural Science Foundation of China (NSFC)[U20A20223] ; National Natural Science Foundation of China (NSFC)[62076236] ; National Natural Science Foundation of China (NSFC)[61721004] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000863094500012 |
| 资助机构 | National Key Research and Development Program ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50308]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Zhang, Ming-Liang |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, 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 | Zhang, Ming-Liang,Zhang, Xu-Yao,Wang, Chuang,et al. Towards prior gap and representation gap for long-tailed recognition[J]. PATTERN RECOGNITION,2023,133:12. |
| APA | Zhang, Ming-Liang,Zhang, Xu-Yao,Wang, Chuang,&Liu, Cheng-Lin.(2023).Towards prior gap and representation gap for long-tailed recognition.PATTERN RECOGNITION,133,12. |
| MLA | Zhang, Ming-Liang,et al."Towards prior gap and representation gap for long-tailed recognition".PATTERN RECOGNITION 133(2023):12. |
入库方式： OAI收割
来源：自动化研究所
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