Unsupervised Anomaly Detection Using Style Distillation
Autoencoders (AEs) have been widely used for unsupervised anomaly detection. They learn from normal samples such that they produce high reconstruction errors for anomalous samples. However, AEs can exhibit the over-detection issue because they imperfectly reconstruct not only anomalous samples but a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9288772/ |