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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14469 |
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| _version_ | 1866914246036553728 |
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| author | Liznerski, Philipp Varshneya, Saurabh Calikus, Ece Wang, Puyu Bartscher, Alexander Vollmer, Sebastian Josef Fellenz, Sophie Kloft, Marius |
| author_facet | Liznerski, Philipp Varshneya, Saurabh Calikus, Ece Wang, Puyu Bartscher, Alexander Vollmer, Sebastian Josef Fellenz, Sophie Kloft, Marius |
| contents | Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates multiple alternative modifications for each anomaly, capturing diverse concepts of anomalousness. Each modification is trained to be perceived as normal by the anomaly detector. The method provides a semantic explanation of the mechanism that triggered the detector, allowing users to explore ``what-if scenarios.'' Qualitative and quantitative analyses across various image datasets demonstrate that applying this method to state-of-the-art detectors provides high-quality semantic explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reimagining Anomalies: What If Anomalies Were Normal? Liznerski, Philipp Varshneya, Saurabh Calikus, Ece Wang, Puyu Bartscher, Alexander Vollmer, Sebastian Josef Fellenz, Sophie Kloft, Marius Computer Vision and Pattern Recognition Machine Learning Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates multiple alternative modifications for each anomaly, capturing diverse concepts of anomalousness. Each modification is trained to be perceived as normal by the anomaly detector. The method provides a semantic explanation of the mechanism that triggered the detector, allowing users to explore ``what-if scenarios.'' Qualitative and quantitative analyses across various image datasets demonstrate that applying this method to state-of-the-art detectors provides high-quality semantic explanations. |
| title | Reimagining Anomalies: What If Anomalies Were Normal? |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.14469 |