Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences between in-distribution (ID) and OOD samples are generated by each processing step inside DNNs. We experimentally clarify this issue by investigating the layer dependence of feature representations from multiple perspectives. We find that intrinsic low dimensionalization of DNNs is essential for understanding how OOD samples become more distinct from ID samples as features propagate to deeper layers. Based on these observations, we provide a simple picture that consistently explains various properties of OOD samples. Specifically, low-dimensional weights eliminate most information from OOD samples, resulting in misclassifications due to excessive attention to dataset bias. In addition, we demonstrate the utility of dimensionality by proposing a dimensionality-aware OOD detection method based on alignment of features and weights, which consistently achieves high performance for various datasets with lower computational cost.

本文解决了深度神经网络（DNN）在分布外（OOD）样本检测中的信息丢失问题。研究表明，DNN内在的低维化特性使OOD样本与正常样本在特征传播过程中变得更加明显，并提出了一种基于特征与权重对齐的维度感知的OOD检测方法，显著降低计算成本的同时提高了检测准确率。

维度引起的深度神经网络异常值信息损失