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WeShort: Out-of-distribution Detection With Weak Shortcut structure

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, detecting whether input comes from an out-of-distribution(OOD) is key to ensuring the safety of neural networks in the real world. In this paper, we propose a simple and effective post-hoc technique, WeShort, to reduce the overconﬁdence of neural networks in 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. Our method is compatible with different OOD detection methods and can generalize well to different architectures of networks. We demonstrate our method in various OOD datasets to show its competitive performance and to provide reasonable hypotheses and dis-cussions 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.