论文标题
为什么摩alano虫距离有效地检测异常？
Why is the Mahalanobis Distance Effective for Anomaly Detection?
论文作者
论文摘要
基于Mahalanobis距离的置信度得分是一种用于预训练的神经分类器的最近提出的异常检测方法，可在分布式分布（OOD）和对抗性实例检测方面达到最新的性能。这项工作分析了为什么这种方法在实践环境中表现出如此强大的性能，同时施加了令人难以置信的假设。也就是说，预训练特征的阶级条件分布与协方差有关。尽管据称基于Mahalanobis的基于距离的方法是通过分类预测信心激发的，但我们发现其出色的性能源于对分类无用的信息。这表明，Mahalanobis置信度得分如此之好的原因是错误的，并且利用了Odin的不同信息，Odin是基于预测置信度的另一种流行的OOD检测方法。这种观点促使我们结合了这两种方法，组合的检测器表现出改善的性能和鲁棒性。这些发现提供了对响应异常输入的神经分类器的行为的洞察力。
The Mahalanobis distance-based confidence score, a recently proposed anomaly detection method for pre-trained neural classifiers, achieves state-of-the-art performance on both out-of-distribution (OoD) and adversarial examples detection. This work analyzes why this method exhibits such strong performance in practical settings while imposing an implausible assumption; namely, that class conditional distributions of pre-trained features have tied covariance. Although the Mahalanobis distance-based method is claimed to be motivated by classification prediction confidence, we find that its superior performance stems from information not useful for classification. This suggests that the reason the Mahalanobis confidence score works so well is mistaken, and makes use of different information from ODIN, another popular OoD detection method based on prediction confidence. This perspective motivates us to combine these two methods, and the combined detector exhibits improved performance and robustness. These findings provide insight into the behavior of neural classifiers in response to anomalous inputs.
