Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However, OOD detection in the multi-label classification task, a more common real-world use case, remains an underexplored domain. In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution) and irrelevant objects (e.g., OOD objects) in images that contain multiple objects belonging to different class categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.

翻译：分布外检测因其在部署系统中的重要性，近年来吸引了机器学习研究界的广泛关注。以往的研究大多聚焦于多分类任务中的分布外样本检测。然而，多标签分类任务（一种更常见的现实应用场景）中的分布外检测仍是一个未被充分探索的领域。本研究提出YolOOD方法——一种利用目标检测领域概念实现多标签分类任务中分布外检测的技术。目标检测模型具有内在能力，能在包含多个不同类别对象的图像中区分感兴趣对象（分布内）与无关对象（例如分布外对象）。这些能力使我们只需进行微小调整，即可将常规目标检测模型转换为具有内在分布外检测能力的图像分类器。我们将所提方法与当前最优的分布外检测方法进行对比，并在全面的分布内和分布外基准数据集上展示了YolOOD超越这些方法的性能。