\noindent Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning models. Extensive work has focused on devising various scoring functions for detecting OOD samples, while only a few studies focus on training neural networks using certain model calibration objectives, which often lead to a compromise in predictive accuracy and support only limited choices of scoring functions. In this work, we first identify the feature collapse phenomena in Logit Normalization (LogitNorm), then propose a novel hyperparameter-free formulation that significantly benefits a wide range of post-hoc detection methods. To be specific, we devise a feature distance-awareness loss term in addition to LogitNorm, termed ELogitNorm, which enables improved OOD detection and in-distribution (ID) confidence calibration. Extensive experiments across standard benchmarks demonstrate that our approach outperforms state-of-the-art training-time methods in OOD detection while maintaining strong ID classification accuracy. Our code is available on: https://github.com/limchaos/ElogitNorm.
@article{arxiv.2504.11434,
title = {Enhancing Out-of-Distribution Detection with Extended Logit Normalization},
author = {Yifan Ding and Xixi Liu and Jonas Unger and Gabriel Eilertsen},
journal= {arXiv preprint arXiv:2504.11434},
year = {2026}
}