ReAct: Out-of-distribution Detection With Rectified Activations
This addresses the challenge of enhancing the safe deployment of neural networks by improving OOD detection, though it appears incremental as it builds on existing methods with a novel activation-based approach.
The paper tackles the problem of neural networks being overconfident on out-of-distribution (OOD) data, which hinders safe deployment, by proposing ReAct, a technique that reduces this overconfidence and achieves a 25.05% reduction in false positive rate on ImageNet compared to the previous best method.
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.