Contextualised Out-of-Distribution Detection using Pattern Identication
Romain Xu-Darme Julien Girard-Satabin Darryl Hond Gabriele Incorvaia Zakaria Chihani
In this work, we propose CODE, an extension of existing work from the field
of explainable AI that identifies class-specific recurring patterns 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, pattern identification allows us
to provide images from the In-Distribution (ID) dataset as reference data to
provide additional context to the confidence 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.