  Out-of-distribution (OOD) detection is a critical task for ensuring the
reliability and safety of deep neural networks in real-world scenarios.
Different from most previous OOD detection methods that focus on designing OOD
scores or introducing diverse outlier examples to retrain the model, we delve
into the obstacle factors in OOD detection from the perspective of typicality
and regard the feature's high-probability region of the deep model as the
feature's typical set. We propose to rectify the feature into its typical set
and calculate the OOD score with the typical features to achieve reliable
uncertainty estimation. The feature rectification can be conducted as a
{plug-and-play} module with various OOD scores. We evaluate the superiority of
our method on both the commonly used benchmark (CIFAR) and the more challenging
high-resolution benchmark with large label space (ImageNet). Notably, our
approach outperforms state-of-the-art methods by up to 5.11$\%$ in the average
FPR95 on the ImageNet benchmark.
