Title: Rethinking the Identification Capability of Out-of-Distribution Detection
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Out-of-distribution Detection, Trustworthy Machine Learning

Abstract: Out-of-distribution (OOD) detection aims to identify semantically shifted data, i.e., samples outside the categories of in-distribution (ID) training data. However, prior studies primarily focus on detecting perfect ID and OOD data, where they exhibit significant covariate shift, i.e., differing distributions of input images with the same semantic label, overlooking the imperfect data setup in which the covariate shift may be negligible. Consequently, a significant covariate shift between ID and OOD samples can serve as a shortcut for OOD detectors, leading to doubts about whether existing OOD detectors truly identify semantic shifts or merely exhibit good performance due to the covariate shift. In response to such doubts, we conduct a theoretical analysis that demonstrates the learnability of the OOD detection task degrades with decreasing covariate shift. In this paper, we investigate a novel and challenging problem termed Covariate-Shift-Free Setting (CSFS), aiming to perform OOD detection in the case of ruling out the impact of covariate shift. To address the CSFS, we propose a novel approach that leverages class-specific gradients as an efficient signal to disentangle OOD features from imperfect ID data for model fine-tuning. Extensive experiments show that our method, using only imperfect ID data, outperforms all existing counterparts, including those employing additional OOD data for model training, across various OOD detection setups.

TL;DR: This study enhances the robustness of the OOD detector by excluding covariate distribution during training, thereby eliminating its influence on OOD detection.