Fixing Data Augmentations for Out-of-distribution Detection

Haipeng Xiong, Kai Xu, Angela Yao

Keywords: OOD Detection; Data Augmentation

Abstract: Out-of-distribution (OOD) detection methods, especially post-hoc methods, rely on off-the-shelf pre-trained models. Existing literature shows how OOD and ID performance are correlated, i.e. stronger models with better ID performance tend to perform better in OOD detection. However, significant performance discrepancies exist between model versions, sometimes exceeding the impact of the OOD detection methods themselves. In this study, we systematically investigated this issue and identified two main factors—label smoothing and mixup—that, while improving in-distribution accuracy, lead to a decline in OOD detection performance. We provide empirical and theoretical explanations for this phenomenon and propose a solution that enhances OOD Detection while maintaining strong in-distribution performance. Code will be released upon acceptance.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 1533