Feature-Alteration Robustness for Out-of-Distribution Detection

Summary

The paper proposes Feature-alteration Robustness (FAR), a method for out-of-distribution (OOD) detection. It rests on the observation that a pretrained in-distribution model stays robust when its intermediate feature maps are altered, while OOD features are heavily distorted. FAR alters intermediate feature maps and measures foreground-background deviations after several layers, and the authors report state-of-the-art results on various benchmarks, alone and combined with ASH.