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

doi:10.1109/TKDE.2021.3139916

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	Adaptive Memory Networks With Self-Supervised Learning for Unsupervised Anomaly Detection
	Zhang, Yuxin 1,2,3; Wang, Jindong 4; Chen, Yiqiang 2,3,5; Yu, Han 6; Qin, Tao 4
	2023-12-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:12 页码:12068-12080
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.
关键词	Unsupervised anomaly detection time series self-supervised learning memory network
DOI	10.1109/TKDE.2021.3139916
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001105152100045
出版者	IEEE COMPUTER SOC
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38813
专题	中国科学院计算技术研究所
通讯作者	Wang, Jindong; Chen, Yiqiang
作者单位	1.Global Energy Interconnect Dev & Cooperat Org, Beijing 100031, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100045, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Microsoft Res, Beijing 100080, Peoples R China 5.Peng Cheng Lab PCL, Shenzhen 518066, Peoples R China 6.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
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.