Beyond AUROC & co. for evaluating out-of-distribution detection
  performance
Galadrielle Humblot-Renaux Sergio Escalera Thomas B. Moeslund
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