Out-of-distribution (OOD) detection is essential for ensuring the reliability and safety of machine learning systems. In recent years, it has received increasing attention, particularly through post-hoc detection and training-based methods. In this paper, we focus on post-hoc OOD detection, which enables identifying OOD samples without altering the model's training procedure or objective. Our primary goal is to investigate the relationship between model capacity and its OOD detection performance. Specifically, we aim to answer the following question: Does the Double Descent phenomenon manifest in post-hoc OOD detection? This question is crucial, as it can reveal whether overparameterization, which is already known to benefit generalization, can also enhance OOD detection. Despite the growing interest in these topics by the classic supervised machine learning community, this intersection remains unexplored for OOD detection. We empirically demonstrate that the Double Descent effect does indeed appear in post-hoc OOD detection. Furthermore, we provide theoretical insights to explain why this phenomenon emerges in such setting. Finally, we show that the overparameterized regime does not yield superior results consistently, and we propose a method to identify the optimal regime for OOD detection based on our observations.

翻译：分布外检测对于确保机器学习系统的可靠性和安全性至关重要。近年来，该领域受到越来越多的关注，特别是通过后验检测和基于训练的方法。本文聚焦于后验分布外检测，该方法能够在无需改变模型训练过程或目标的情况下识别分布外样本。我们的主要目标是探究模型容量与其分布外检测性能之间的关系。具体而言，我们旨在回答以下问题：双下降现象是否在后验分布外检测中显现？这一问题至关重要，因为它可以揭示已知有利于泛化的过参数化是否也能增强分布外检测。尽管经典监督机器学习社区对这些主题的兴趣日益增长，但这一交叉领域在分布外检测中仍未得到探索。我们通过实证证明，双下降效应确实出现在后验分布外检测中。此外，我们提供理论洞见来解释为何该现象在此类设置中出现。最后，我们表明过参数化机制并非始终产生更优结果，并基于我们的观察提出一种方法来识别分布外检测的最佳机制。