Standard recognition approaches are unable to deal with novel categories at test time. Their overconfidence on the known classes makes the predictions unreliable for safety-critical applications such as healthcare or autonomous driving. Out-Of-Distribution (OOD) detection methods provide a solution by identifying semantic novelty. Most of these methods leverage a learning stage on the known data, which means training (or fine-tuning) a model to capture the concept of normality. This process is clearly sensitive to the amount of available samples and might be computationally expensive for on-board systems. A viable alternative is that of evaluating similarities in the embedding space produced by large pre-trained models without any further learning effort. We focus exactly on such a fine-tuning-free OOD detection setting. This works presents an in-depth analysis of the recently introduced relational reasoning pre-training and investigates the properties of the learned embedding, highlighting the existence of a correlation between the inter-class feature distance and the OOD detection accuracy. As the class separation depends on the chosen pre-training objective, we propose an alternative loss function to control the inter-class margin, and we show its advantage with thorough experiments.

翻译：标准识别方法无法在测试阶段处理新类别。由于对已知类别的过度自信，其预测在医疗健康或自动驾驶等安全关键型应用中不可靠。分布外（OOD）检测方法通过识别语义新颖性提供了解决方案。多数此类方法借助已知数据上的学习阶段，即通过训练（或微调）模型来捕捉正常性概念。这一过程对可用样本数量高度敏感，且可能在车载系统中计算开销巨大。另一种可行的替代方案是在无需额外学习的前提下，评估大型预训练模型所生成嵌入空间中的相似性。我们正是聚焦于此种无需微调的OOD检测设置。本文深入分析了近期提出的关系推理预训练方法，探究了所学嵌入的特性，揭示了类间特征距离与OOD检测准确性之间的相关性。由于类别间隔取决于所选的预训练目标，我们提出了一种替代损失函数来控制类间间隔，并通过充分实验展示了其优势。