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arXiv · 2603.07571

A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification

Furkan Genç·Onat Özdemir·Emre Akbaş
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Abstract

Out-of-distribution (OOD) detection is critical in safety-sensitive applications. While this challenge has been addressed from various perspectives, the influence of training objectives on OOD behavior remains comparatively underexplored. In this paper, we present a systematic comparison of four widely used training objectives: Cross-Entropy Loss, Prototype Loss, Triplet Loss, and Average Precision (AP) Loss, spanning probabilistic, prototype-based, metric-learning, and ranking-based supervision, for OOD detection in image classification under standardized OpenOOD protocols. Within the evaluated ResNet-18/OpenOOD setting and objective-specific OOD scoring rules, Cross-Entropy Loss, Prototype Loss, and AP Loss achieve comparable in-distribution accuracy, while Cross-Entropy Loss provides the most consistent near- and far-OOD AUROC overall; the other objectives can be competitive in specific settings.

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Furkan Genç, Onat Özdemir, Emre Akbaş. 2026-03-08. A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification. https://arxiv.org/abs/2603.07571

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