SLE: Out-of-Distribution Detection With Shallow Layer-Driven Enhancement

Out-of-distribution detection aims to protect models against overconfidently categorizing samples from unknown categories, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, out-of-distribution data (OOD), into known categories, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, in-distribution data (ID). From the perspective of feature distribution, the difference between OOD samples and ID samples can be decomposed into semantic shifts and covariate shifts. Most DL-based methods only extract deeper features, which represent semantic shifts, to discern feature variances in the data, ignoring the exploration of covariance shifts. In this paper, we propose a Shallow Layer-driven Enhanced OOD detection method (SLE), which enhances the difference of OOD samples by exploiting covariate shifts in shallow features. Specifically, it contains three main components: Hierarchical Feature Extractor (HFE), Adaptive Dimensionality Reduction Strategy (ADR), Cross-layer Score Aggregator (CSA). HFE is responsible for extracting both deeper and shallow features from the deep network. ADR adaptively reduces all hierarchical feature dimensionality according to sample characteristics, avoiding feature redundancy. CSA defines a novel confidence score for OOD samples, that effectively prevents confusion in the feature representation space at each layer. In SLE, these three closely related components cooperate with each other to effectively enhance the representation ability of OOD samples and divide OOD data better. We conduct extensive experiments to examine the performance of SLE in four benchmarks and discuss its individual components. This method performs well on the OOD datasets.