Title: Concept-based Explanations for Out-of-Distribution Detectors. Abstract: Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers.
While a myriad of methods have focused on improving the performance of OOD detectors, a critical gap remains in interpreting their decisions.
We help bridge this gap by providing explanations for OOD detectors based on learned high-level concepts.
We first propose two new metrics for assessing the effectiveness of a particular set of concepts for explaining OOD detectors: 1) $\textit{detection completeness}$, which quantifies the sufficiency of concepts for explaining an OOD-detector's decisions, and 2) $\textit{concept separability}$, which captures the distributional separation between in-distribution and OOD data in the concept space.
Based on these metrics, we propose a framework for learning a set of concepts that satisfy the desired properties of detection completeness and concept separability, and demonstrate the framework's effectiveness in providing concept-based explanations for diverse OOD detection techniques.
We also show how to identify prominent concepts that contribute to the detection results via a modified Shapley value-based importance score.