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:由于机器学习模型在不同任务中继续取得令人印象深刻的成绩,有效发现异常情况对于这些模型的重要性也有所增加,众所周知,即使经过良好训练的模型也丧失了在分配外投入中有效发挥作用的能力,因此,最近对分配外(OOOD)的探测受到了一些注意,在绝大多数情况下,它使用培训数据集估计的分布来探测OOD。我们证明,目前的探测器继承了培训数据集中的偏差,不幸的是,这是一个严重障碍,可能限制所培训模型的效用。这可以使现有的OOD探测器无法用于培训分发之外的投入,但具有同样的语义信息(例如培训类标签)。为了纠正这种情况,我们首先确定什么是理想的OOD,将输入与其语义信息内容联系起来。我们用OOD检测从MNIST和COCO数据集的培训数据中提取的语义信息,并表明它不仅减少虚假的警报,而且大大改进对OOD投入的探测,其培训数据具有尖锐的特征。