Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples
Abstract Industrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a meth...
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| Auteurs principaux: | , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Springer
2023-09-01
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| Collection: | International Journal of Computational Intelligence Systems |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/s44196-023-00328-0 |
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