Unsupervised anomaly detection and localization with one model for all category

Author:

Tan PengjieORCID,Wong Wai KeungORCID

Publisher

Elsevier BV

Reference48 articles.

1. A hybrid methodology for anomaly detection in Cyber–Physical Systems;Jeffrey;Neurocomputing,2024

2. Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines;Dhiman;IEEE Trans. Energy Convers.,2021

3. Anas Al-lahham, Nurbek Tastan, Muhammad Zaigham Zaheer, Karthik Nandakumar, A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 6793–6802.

4. A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions;Yan;IEEE Access,2024

5. Anomaly detection in crowd scene;Wang,2010

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