Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics

Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness 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-based robust gradient statistics.

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.