Out-of-distribution detection with non-semantic exploration

Abstract

Out-of-distribution (OOD) detection is crucial in modern deep learning applications, as it can identify OOD data drawn from distributions differing from those of the in-distribution (ID) data. Advanced OOD detection methods primarily rely on post-hoc strategies, which identify OOD data by analyzing the predictions of a model well-trained on ID data. However, deep models are known to be impacted by spurious features such as backgrounds, causing existing OOD detection methods to fail in identifying OOD data that share the same spurious features as ID data. Therefore, this paper studies how to mitigate spurious features to improve OOD detection. To address this challenge, we propose a novel method called Non-semantic Exploration OOD Detection (NsED), which focuses on exploring and exploiting non-semantic features. In particular, NsED first explores non-semantic features in an OOD generalization manner. These non-semantic features are then used to train deep models to be more robust against spurious features. Through extensive experiments on representative benchmarks, we show that NsED significantly and consistently improves the detection performance of many representative post-hoc OOD detection methods.