中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Dual Compensation Residual Networks for Class Imbalanced Learning

文献类型：期刊论文

作者Hou, Ruibing1,2; Chang, Hong1,2; Ma, Bingpeng3; Shan, Shiguang1,2; Chen, Xilin1,2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-10-01
卷号45期号:10页码:11733-11752
关键词Class imbalance learning class-incremental learning residual path
ISSN号0162-8828
DOI10.1109/TPAMI.2023.3275585
英文摘要Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution, which is prone to overfitting on tail classes and underfitting on head classes. In this work, we propose Dual Compensation Residual Networks to better fit both tail and head classes. First, we propose dual Feature Compensation Module (FCM) and Logit Compensation Module (LCM) to alleviate the overfitting issue. The design of these two modules is based on the observation: an important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. In details, the test features of a tail category tend to drift towards feature cloud of multiple similar head categories. So FCM estimates a multi-mode feature drift direction for each tail category and compensate for it. Furthermore, LCM translates the deterministic feature drift vector estimated by FCM along intra-class variations, so as to cover a larger effective compensation space, thereby better fitting the test features. Second, we propose a Residual Balanced Multi-Proxies Classifier (RBMC) to alleviate the under-fitting issue. Motivated by the observation that re-balancing strategy hinders the classifier from learning sufficient head knowledge and eventually causes underfitting, RBMC utilizes uniform learning with a residual path to facilitate classifier learning. Comprehensive experiments on Long-tailed and Class-Incremental benchmarks validate the efficacy of our method.
资助项目National Key Ramp;D Program of China ; Natural Science Foundation of China(NSFC)[2018AAA0102402] ; Natural Science Foundation of China(NSFC)[61976203] ; Natural Science Foundation of China(NSFC)[62276246] ; National Postdoctoral Program for Innovative Talents[U19B2036] ; [BX20220310]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001068816800018
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/21139]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hou, Ruibing
作者单位1.Univ Chinese Acad Sci, Beijing 5100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol ICT, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Hou, Ruibing,Chang, Hong,Ma, Bingpeng,et al. Dual Compensation Residual Networks for Class Imbalanced Learning[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(10):11733-11752.
APA Hou, Ruibing,Chang, Hong,Ma, Bingpeng,Shan, Shiguang,&Chen, Xilin.(2023).Dual Compensation Residual Networks for Class Imbalanced Learning.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(10),11733-11752.
MLA Hou, Ruibing,et al."Dual Compensation Residual Networks for Class Imbalanced Learning".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.10(2023):11733-11752.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。