Reimagining Anomalies: What If Anomalies Were Normal?

• URL: http://arxiv.org/abs/2402.14469v1
• Date: Thu, 22 Feb 2024 11:56:44 GMT
• Title: Reimagining Anomalies: What If Anomalies Were Normal?
• Authors: Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, and Marius Kloft
• Abstract summary: We introduce a novel explanation method that generates multiple counterfactual examples for each anomaly. A counterfactual example is a modification of the anomaly that is perceived as normal by the anomaly detector. The method provides a high-level semantic explanation of the mechanism that triggered the anomaly detector, allowing users to explore "what-if scenarios"
• Score: 21.480869966442143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 counterfactual examples for each anomaly, capturing diverse concepts of anomalousness. A counterfactual example is a modification of the anomaly that is perceived as normal by the anomaly detector. The method provides a high-level semantic explanation of the mechanism that triggered the anomaly detector, allowing users to explore "what-if scenarios." Qualitative and quantitative analyses across various image datasets show that the method applied to state-of-the-art anomaly detectors can achieve high-quality semantic explanations of detectors.

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