Constrained unsupervised anomaly segmentation
Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. To address the limitations of residual-based anomaly localization, v...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |