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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 11
Hauptverfasser:	Huang, Wei, Zhang, Bingyang, Zhang, Kaituo, Gao, Hua, Wan, Rongchun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!