AAAI 2026technical0 citations
Reimagining Anomalies: What If Anomalies Were Normal?
Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Puyu Wang, Alexander Bartscher, Sebastian Josef Vollmer, Sophie Fellenz, Marius Kloft
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 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.
BibTeX
@inproceedings{aaai2026_reimagininganoma,
title = {Reimagining Anomalies: What If Anomalies Were Normal?},
author = {Philipp Liznerski and Saurabh Varshneya and Ece Calikus and Puyu Wang and Alexander Bartscher and Sebastian Josef Vollmer and Sophie Fellenz and Marius Kloft},
booktitle = {AAAI 2026},
year = {2026}
}