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Contrastive Correlation Preserving Replay for Online Continual Learning

文献类型：期刊论文

作者Yu, Da4; Zhang, Mingyi2,3; Li, Mantian4; Zha, Fusheng4; Zhang, Junge2,3; Sun, Lining4; Huang, Kaiqi1,2,3
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2024
卷号34期号:1页码:124-139
ISSN号1051-8215
关键词Task analysis Correlation Knowledge transfer Training Memory management Data models Mutual information Continual learning catastrophic forgetting class-incremental learning experience replay
DOI10.1109/TCSVT.2023.3285221
通讯作者Zha, Fusheng(zhafusheng@hit.edu.cn) ; Zhang, Junge(jgzhang@nlpr.ia.ac.cn)
英文摘要Online Continual Learning (OCL), as a core step towards achieving human-level intelligence, aims to incrementally learn and accumulate novel concepts from streaming data that can be seen only once, while alleviating catastrophic forgetting on previously acquired knowledge. Under this mode, the model needs to learn new classes or tasks in an online manner, and the data distribution may change over time. Moreover, task boundaries and identities are not available during training and evaluation. To balance the stability and plasticity of networks, in this work, we propose a replay-based framework for OCL, named Contrastive Correlation Preserving Replay (CCPR), which focuses on not only instances but also correlations between multiple instances. Specifically, besides the previous raw samples, the corresponding representations are stored in the memory and used to construct correlations for the past and the current model. To better capture correlation and higher-order dependencies, we maximize the low bound of mutual information between the past correlation and the current correlation by leveraging contrastive objectives. Furthermore, to improve the performance, we propose a new memory update strategy, which simultaneously encourages the balance and diversity of samples within the memory. With limited memory slots, it allows less redundant and more representative samples for later replay. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms the state-of-the-art methods and can effectively consolidate knowledge to alleviate forgetting.
WOS关键词KNOWLEDGE
资助项目National Natural Science Foundation of China
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001138814400038
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/55545]  
专题复杂系统认知与决策实验室
通讯作者Zha, Fusheng; Zhang, Junge
作者单位1.CAS Ctr Excellence Brain Sci & Intelligence Techno, Shanghai 200031, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Syst & Engn, Beijing 100190, Peoples R China
4.Harbin Inst Technol HIT, State Key Lab Robot & Syst, Harbin 150080, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Da,Zhang, Mingyi,Li, Mantian,et al. Contrastive Correlation Preserving Replay for Online Continual Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2024,34(1):124-139.
APA Yu, Da.,Zhang, Mingyi.,Li, Mantian.,Zha, Fusheng.,Zhang, Junge.,...&Huang, Kaiqi.(2024).Contrastive Correlation Preserving Replay for Online Continual Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,34(1),124-139.
MLA Yu, Da,et al."Contrastive Correlation Preserving Replay for Online Continual Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34.1(2024):124-139.

入库方式： OAI收割

来源：自动化研究所

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