Model-Free Test Time Adaptation for Out-of-Distribution Detection.

Abstract

Out-of-distribution (OOD) detection is essential for the reliability of ML models. Most existing methods for OOD detection learn a fixed decision criterion from a given in-distribution dataset and apply it universally to decide if a data point is OOD. Recent work Fang et al. (2022) shows that given only in-distribution data, it is impossible to reliably detect OOD data without extra assumptions. Motivated by the theoretical result and recent exploration of test-time adaptation methods, we propose a Non-Parametric Test Time Adaptation framework for Out-Of-Distribution Detection (AdaODD). Unlike conventional methods, AdaODD utilizes online test samples for model adaptation during testing, enhancing adaptability to changing data distributions. The framework incorporates detected OOD instances into decision-making, reducing false positive rates, particularly when ID and OOD distributions overlap significantly. We demonstrate the effectiveness of AdaODD through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that AdaODD significantly improves the performance of OOD detection over state-of-the-art methods. Specifically, AdaODD reduces the false positive rate (FPR95) by 23.23% on the CIFAR-10 benchmarks and 38% on the ImageNet-1 k benchmarks compared to the advanced methods. Lastly, we theoretically verify the effectiveness of AdaODD.