Reimagining Anomalies: What If Anomalies Were Normal?
<|reference_start|>Reimagining Anomalies: What If Anomalies Were Normal?: 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.<|reference_end|>
@article{liznerski2024reimagining,
    title={Reimagining Anomalies: What If Anomalies Were Normal?},
    author={Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, and
  Marius Kloft},
    journal={arXiv preprint arXiv:2402.14469},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14469},
    primaryClass={cs.CV cs.LG stat.ML}
}