An Adversarial Time-Frequency Reconstruction Network for Unsupervised Anomaly Detection.

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作者： Jin,Fan ^[1] ; Zehao,Wang ^[2] ; Huifeng,Wu ^[3] ; Danfeng,Sun ^[2] ; Jia,Wu ^[4] ; Xin,Lu ^[5]

作者单位： Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, China. ^[1] Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China. ^[2] Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China. Electronic address: whf@hdu.edu.cn. ^[3] Department of Computing, Macquarie University, Sydney, Australia. ^[4] School of Computer Science, Leeds Trinity University, United Kingdom. ^[5]

关键词 Anomaly detection Neural networks Unsupervised anomaly detection

主题词女(雌)性(Female);妊娠(Pregnancy);人类(Humans);学习(Learning);时间因素(Time Factors)

DOI 10.1016/j.neunet.2023.09.018

PMID 37741104

发布时间 2023-11-13

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