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Out-of-distribution (OOD) detection has received much attention lately due toits practical importance in enhancing the safe deployment of neural networks.One of the primary challenges is that models often produce highly confidentpredictions on OOD data, which undermines the driving principle in OODdetection that the model should only be confident about in-distributionsamples. In this work, we propose ReAct--a simple and effective technique forreducing model overconfidence on OOD data. Our method is motivated by novelanalysis on internal activations of neural networks, which displays highlydistinctive signature patterns for OOD distributions. Our method can generalizeeffectively to different network architectures and different OOD detectionscores. We empirically demonstrate that ReAct achieves competitive detectionperformance on a comprehensive suite of benchmark datasets, and givetheoretical explication for our method's efficacy. On the ImageNet benchmark,ReAct reduces the false positive rate (FPR95) by 25.05% compared to theprevious best method.

ReAct: Out-of-distribution Detection With Rectified Activations