基于选择性推断量化深层最近邻异常检测的统计显著性

在实际应用中，异常检测 (AD) 常常没有可用的异常数据， necessitating semi-supervised methods that rely solely on normal data。 Among these methods, deep k-nearest neighbor (deep kNN) AD stands out for its interpretability and flexibility, leveraging distance-based scoring in deep latent spaces。 Despite its strong performance, deep kNN lacks a mechanism to quantify uncertainty-an essential feature for critical applications such as industrial inspection。 To address this limitation, we propose a statistical framework that quantifies the significance of detected anomalies in the form of p-values, thereby enabling control over false positive rates at a user-specified significance level (e.g.,0.05)。 A central challenge lies in managing selection bias, which we tackle using Selective Inference-a principled method for conducting inference conditioned on data-driven selections。 We evaluate our method on diverse datasets and demonstrate that it provides reliable AD well-suited for industrial use cases。