PT Unknown
AU Galadrielle Humblot-Renaux
   Sergio Escalera
   Thomas B. Moeslund
TI Beyond AUROC & co. for evaluating out-of-distribution detection performance
BT Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
PY 2023
BP 3880
EP 3889
DI 10.1109/CVPRW59228.2023.00402
AB While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric – Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc
ER