Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have been universally used and have demonstrated significant success in detecting anomalies. However, the o...

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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 11
Main Authors:	Huang, Wei, Zhang, Bingyang, Zhang, Kaituo, Gao, Hua, Wan, Rongchun
Format:	Journal Article
Language:	English
Published:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Anomalies Anomaly detection autoencoder Data analysis Data models Datasets deep support vector data description (Deep SVDD) Feature extraction hypersphere collapse Long short term memory LSTM Mathematical models Modules Training Vectors
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
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