Abstract
In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring pat- terns to build a robust Out-of-Distribution (OoD) detection method for visual classifiers. CODE does not require any classifier retraining and is OoD-agnostic, i.e., tuned directly to the training dataset. Crucially, pat- tern identification allows us to provide images from the In-Distribution (ID) dataset as reference data to provide additional context to the con- fidence scores. In addition, we introduce a new benchmark based on perturbations of the ID dataset that provides a known and quantifiable measure of the discrepancy between the ID and OoD datasets serving as a reference value for the comparison between OoD detection methods.
