  Neural networks have achieved impressive performance for data in the
distribution which is the same as the training set but can produce an
overconfident incorrect result for the data these networks have never seen.
Therefore, it is essential to detect whether inputs come from
out-of-distribution(OOD) in order to guarantee the safety of neural networks
deployed in the real world. In this paper, we propose a simple and effective
post-hoc technique, WeShort, to reduce the overconfidence of neural networks on
OOD data. Our method is inspired by the observation of the internal residual
structure, which shows the separation of the OOD and in-distribution (ID) data
in the shortcut layer. Our method is compatible with different OOD detection
scores and can generalize well to different architectures of networks. We
demonstrate our method on various OOD datasets to show its competitive
performances and provide reasonable hypotheses to explain why our method works.
On the ImageNet benchmark, Weshort achieves state-of-the-art performance on the
false positive rate (FPR95) and the area under the receiver operating
characteristic (AUROC) on the family of post-hoc methods.
