🤖 AI Summary
This study systematically investigates the impact of different training objectives on out-of-distribution (OOD) detection performance in image classification, with the aim of enhancing model robustness in safety-critical applications. Under the unified OpenOOD evaluation protocol, it presents the first comprehensive assessment of cross-entropy loss, prototype loss, triplet loss, and mean average precision loss across both near- and far-OOD detection settings on CIFAR-10/100 and ImageNet-200. The findings reveal that cross-entropy loss consistently achieves the most robust and reliable OOD detection performance while maintaining high in-distribution accuracy. Nevertheless, alternative objectives also demonstrate competitive results under specific configurations, offering empirical guidance for selecting training objectives tailored to OOD detection tasks.
📝 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. Across CIFAR-10/100 and ImageNet-200, we find that 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 performance overall; the other objectives can be competitive in specific settings.