ReAct: Out-of-distribution Detection With Rectified Activations

TL;DR

ReAct is a simple technique that reduces neural network overconfidence on out-of-distribution data by analyzing internal activations, leading to improved detection performance across various benchmarks.

Contribution

The paper introduces ReAct, a novel method based on activation analysis, that effectively improves out-of-distribution detection across different models and datasets.

Findings

ReAct reduces FPR95 by 25.05% on ImageNet.

ReAct generalizes well to different architectures and OOD scores.

Theoretical analysis supports ReAct's effectiveness.

Abstract

Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks. One of the primary challenges is that models often produce highly confident predictions on OOD data, which undermines the driving principle in OOD detection that the model should only be confident about in-distribution samples. In this work, we propose ReAct--a simple and effective technique for reducing model overconfidence on OOD data. Our method is motivated by novel analysis on internal activations of neural networks, which displays highly distinctive signature patterns for OOD distributions. Our method can generalize effectively to different network architectures and different OOD detection scores. We empirically demonstrate that ReAct achieves competitive detection performance on a comprehensive suite of benchmark datasets, and give theoretical explication for our method's efficacy. On the ImageNet benchmark, ReAct reduces the false positive rate (FPR95) by 25.05% compared to the previous best method.