The out-of-distribution (OOD) problem generally arises when neural networks
encounter data that significantly deviates from the training data distribution,
\ie, in-distribution (InD). In this paper, we study the OOD problem from a
neuron activation view. We first formulate neuron activation states by
considering both the neuron output and its influence on model decisions. Then,
we propose the concept of \textit{neuron activation coverage} (NAC), which
characterizes the neuron behaviors under InD and OOD data. Leveraging our NAC,
we show that 1) InD and OOD inputs can be naturally separated based on the
neuron behavior, which significantly eases the OOD detection problem and
achieves a record-breaking performance of 0.03% FPR95 on ResNet-50,
outperforming the previous best method by 20.67%; 2) a positive correlation
between NAC and model generalization ability consistently holds across
architectures and datasets, which enables a NAC-based criterion for evaluating
model robustness. By comparison with the traditional validation criterion, we
show that NAC-based criterion not only can select more robust models, but also
has a stronger correlation with OOD test performance.

本文提出了一种神经元激活覆盖度（NAC）概念，并通过研究神经元行为以区分 InD 和 OOD 数据，打破了 ResNet-50 FPR95 记录并提出了 NAC 标准以评估模型健壮性。