Contextualised Out-of-Distribution Detection using Pattern Identication

• URL: http://arxiv.org/abs/2311.12855v1
• Date: Tue, 24 Oct 2023 07:55:09 GMT
• Title: Contextualised Out-of-Distribution Detection using Pattern Identication
• Authors: Romain Xu-Darme (LSL, LIG), Julien Girard-Satabin (LSL), Darryl Hond (TRT UK), Gabriele Incorvaia (TRT UK), Zakaria Chihani (LSL)
• Abstract summary: CODE is an extension of existing work from the field of explainable AI. It identifies class-specific recurring patterns to build a robust Out-of-Distribution (OoD) detection method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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