ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be subject to user bias. In this paper, we propose an Anomalous La...

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Bibliographic Details
Main Authors: Hironori Murase, Kenji Fukumizu
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9761923/

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