TSRE: Channel-Aware Typical Set Refinement for Out-of-Distribution Detection

TL;DR

This paper introduces TSRE, a channel-aware typical set refinement method that improves out-of-distribution detection by addressing activation distribution skewness and channel characteristics, achieving state-of-the-art results.

Contribution

The paper proposes a novel typical set refinement technique that considers channel discriminability and activity, along with skewness-based bias mitigation for better OOD detection.

Findings

Achieves state-of-the-art OOD detection performance on ImageNet-1K and CIFAR-100.

Generalizes well across different backbone architectures and scoring functions.

Effectively reduces false positives by refining activation typical sets.

Abstract

Out-of-Distribution (OOD) detection is a critical capability for ensuring the safe deployment of machine learning models in open-world environments, where unexpected or anomalous inputs can compromise model reliability and performance. Activation-based methods play a fundamental role in OOD detection by mitigating anomalous activations and enhancing the separation between in-distribution (ID) and OOD data. However, existing methods apply activation rectification while often overlooking channel's intrinsic characteristics and distributional skewness, which results in inaccurate typical set estimation. This discrepancy can lead to the improper inclusion of anomalous activations across channels. To address this limitation, we propose a typical set refinement method based on discriminability and activity, which rectifies activations into a channel-aware typical set. Furthermore, we introduce a skewness-based refinement to mitigate distributional bias in typical set estimation. Finally, we leverage the rectified activations to compute the energy score for OOD detection. Experiments on the ImageNet-1K and CIFAR-100 benchmarks demonstrate that our method achieves state-of-the-art performance and generalizes effectively across backbones and score functions.