As machine learning models continue to achieve impressive performance across different tasks, the importance of effective anomaly detection for such models has increased as well. It is common knowledge that even well-trained models lose their ability to function effectively on out-of-distribution inputs. Thus, out-of-distribution (OOD) detection has received some attention recently. In the vast majority of cases, it uses the distribution estimated by the training dataset for OOD detection. We demonstrate that the current detectors inherit the biases in the training dataset, unfortunately. This is a serious impediment, and can potentially restrict the utility of the trained model. This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information (e.g. training class labels). To remedy this situation, we begin by defining what should ideally be treated as an OOD, by connecting inputs with their semantic information content. We perform OOD detection on semantic information extracted from the training data of MNIST and COCO datasets and show that it not only reduces false alarms but also significantly improves the detection of OOD inputs with spurious features from the training data.

翻译：随着机器学习模型在不同任务中持续取得显著性能，对此类模型进行有效异常检测的重要性也随之增加。众所周知，即使是训练良好的模型，在面对分布外输入时也会丧失有效运行的能力。因此，分布外（OOD）检测近年来受到一定关注。在绝大多数情况下，OOD检测利用训练数据集估计的分布进行检测。然而，我们证明当前检测方法不幸地继承了训练数据集中的偏差。这是一个严重障碍，可能限制训练模型的实用性，使得现有OOD检测器对位于训练分布之外但包含相同语义信息（如训练类别标签）的输入失效。为解决此问题，我们首先通过将输入与其语义信息内容关联，明确定义理想情况下应被视为分布外的标准。我们针对从MNIST和COCO数据集训练数据中提取的语义信息进行OOD检测，结果表明该方法不仅减少了误报，还显著提升了对包含训练数据虚假特征的分布外输入的检测能力。