Adaptive feature refinement with information preservation for multiclass unsupervised anomaly detection
Abstract Recent advancements in anomaly detection have shown significant potential across various industrial domains. However, a wide range of unpredictable defect types emerge in the real world, and anomaly images are often challenging to obtain, making traditional methods less suitable. To address...
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| Hoofdauteurs: | , , , , , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Taylor & Francis Group
2026-06-01
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| Reeks: | Journal of Information Display |
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/s44469-026-00014-9 |
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