Image classification plays a pivotal role across diverse applications, yet
challenges persist when models are deployed in real-world scenarios. Notably,
these models falter in detecting unfamiliar classes that were not incorporated
during classifier training, a formidable hurdle for safe and effective
real-world model deployment, commonly known as out-of-distribution (OOD)
detection. While existing techniques, like max logits, aim to leverage logits
for OOD identification, they often disregard the intricate interclass
relationships that underlie effective detection. This paper presents an
innovative class relevance learning method tailored for OOD detection. Our
method establishes a comprehensive class relevance learning framework,
strategically harnessing interclass relationships within the OOD pipeline. This
framework significantly augments OOD detection capabilities. Extensive
experimentation on diverse datasets, encompassing generic image classification
datasets (Near OOD and Far OOD datasets), demonstrates the superiority of our
method over state-of-the-art alternatives for OOD detection.

本文介绍了一种针对 out-of-distribution（OOD）检测的创新类相关性学习方法，该方法在 OOD 管道中策略性地利用了类间关系，显著增强了 OOD 检测能力。在包括通用图像分类数据集（近 OOD 和远 OOD 数据集）的多样数据集上进行的大量实验表明，我们的方法在 OOD 检测方面优于现有技术。