A Median Perspective on Unlabeled Data for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2510.06505v1
• Date: Tue, 07 Oct 2025 22:43:57 GMT
• Title: A Median Perspective on Unlabeled Data for Out-of-Distribution Detection
• Authors: Momin Abbas, Ali Falahati, Hossein Goli, Mohammad Mohammadi Amiri,
• Abstract summary: Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness and reliability of machine learning systems.<n>Recent approaches have explored the use of unlabeled data, showing potential for enhancing OOD detection capabilities.<n>We introduce Medix, a novel framework designed to identify potential outliers from unlabeled data using the median operation.
• Score: 5.937613452723966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness and reliability of machine learning systems deployed in real-world applications. Recent approaches have explored the use of unlabeled data, showing potential for enhancing OOD detection capabilities. However, effectively utilizing unlabeled in-the-wild data remains challenging due to the mixed nature of both in-distribution (InD) and OOD samples. The lack of a distinct set of OOD samples complicates the task of training an optimal OOD classifier. In this work, we introduce Medix, a novel framework designed to identify potential outliers from unlabeled data using the median operation. We use the median because it provides a stable estimate of the central tendency, as an OOD detection mechanism, due to its robustness against noise and outliers. Using these identified outliers, along with labeled InD data, we train a robust OOD classifier. From a theoretical perspective, we derive error bounds that demonstrate Medix achieves a low error rate. Empirical results further substantiate our claims, as Medix outperforms existing methods across the board in open-world settings, confirming the validity of our theoretical insights.

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