Explaining anomalies through semi-supervised Autoencoders
This work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations. In particular, we adopt heatmaps as explanations, where a heatmap can be regarded as a collection of per-feature scores. To ex...
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| Auteurs principaux: | , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Elsevier
2025-12-01
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| Collection: | Array |
| Sujets: | |
| Accès en ligne: | http://www.sciencedirect.com/science/article/pii/S259000562500164X |
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