Enhancing Out-of-Distribution Detection with Extended Logit Normalization

Abstract

Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning models. While extensive work has focused on designing effective scoring functions for OOD detection, relatively few studies explore training neural networks with calibration-oriented objectives, which often compromise predictive accuracy and restrict the choice of scoring functions. In this work, we first identify feature collapse in Logit Normalization (LogitNorm), and then propose a novel hyperparameter-free training formulation that significantly improves a wide range of post-hoc detection methods. Specifically, we introduce a feature distance-aware normalization objective, termed ELogitNorm, which enhances both OOD detection performance and in-distribution (ID) confidence calibration. Extensive experiments on standard benchmarks demonstrate that our approach outperforms state-of-the-art training-time methods in OOD detection while preserving strong ID classification performance. Our code is available at: https://github.com/limchaos/ElogitNorm.