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Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection

文献类型：期刊论文

作者Zhang, Yuxin4,5,6; Wang, Jindong3; Chen, Yiqiang2,4,5; Yu, Han1; Qin, Tao3
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023-12-01
卷号35期号:12页码:12068-12080
关键词Unsupervised anomaly detection time series self-supervised learning memory network
ISSN号1041-4347
DOI10.1109/TKDE.2021.3139916
英文摘要Unsupervised anomaly detection aims to build models to effectively detect unseen anomalies by only training on the normal data. Although previous reconstruction-based methods have made fruitful progress, their generalization ability is limited due to two critical challenges. First, the training dataset only contains normal patterns, which limits the model generalization ability. Second, the feature representations learned by existing models often lack representativeness which hampers the ability to preserve the diversity of normal patterns. In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection. Based on the convolutional autoencoder structure, AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations. Experiments on four public multivariate time series datasets demonstrate that AMSL significantly improves the performance compared to other state-of-the-art methods. Specifically, on the largest CAP sleep stage detection dataset with 900 million samples, AMSL outperforms the second-best baseline by 4%+ in both accuracy and F1 score. Apart from the enhanced generalization ability, AMSL is also more robust against input noise.
资助项目National Key Research and Development Plan of China[2020YFC2007104] ; Natural Science Foundation of China[61972383] ; Natural Science Foundation of China[61902377] ; Natural Science Foundation of China[61902379] ; Science and Technology Service Network Initiative, Chinese Academy of Sciences[KFJ-STS-QYZD-2021-11-001] ; National Research Foundation, Singapore under its AI Singapore Programme under Grant AISG[AISG2-RP-2020-019] ; RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund[A20G8b0102] ; Nanyang Assistant Professorship (NAP)
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001105152100045
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38812]  
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, Jindong; Chen, Yiqiang
作者单位1.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
2.Peng Cheng Lab PCL, Shenzhen 518066, Peoples R China
3.Microsoft Res, Beijing 100080, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100045, Peoples R China
6.Global Energy Interconnect Dev & Cooperat Org, Beijing 100031, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yuxin,Wang, Jindong,Chen, Yiqiang,et al. Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(12):12068-12080.
APA Zhang, Yuxin,Wang, Jindong,Chen, Yiqiang,Yu, Han,&Qin, Tao.(2023).Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(12),12068-12080.
MLA Zhang, Yuxin,et al."Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.12(2023):12068-12080.

入库方式： OAI收割

来源：计算技术研究所

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