利用已知无异常区域提升异常检测准确率
Constraining Anomaly Detection with Anomaly-Free Regions
- 引入无异常区域（AFR）约束数据分布估计
- 使用AFR后，随机猜测算法成为强基线
- 适合有先验知识的工业异常检测场景
我们提出新颖的无异常区域（AFR）概念以改进异常检测。AFR是数据空间中已知不含异常的区域，可通过领域知识获得，可包含任意数量的正常数据点，位置不限。其核心优势在于约束非异常数据分布的估计：估计的分布在AFR内的概率质量必须与其中正常数据点的数量一致。基于此，我们建立了坚实的理论基础并提供了参考实现。实验结果表明，借助估计的AFR，基于随机猜测的高效算法成为强基线，多个广泛应用的方法难以超越。在具备真实AFR标注的数据集上，当前最优方法被超越。
原文摘要 · Abstract (English)
We propose the novel concept of anomaly-free regions (AFR) to improve anomaly detection. An AFR is a region in the data space for which it is known that there are no anomalies inside it, e.g., via domain knowledge. This region can contain any number of normal data points and can be anywhere in the data space. AFRs have the key advantage that they constrain the estimation of the distribution of non-anomalies: The estimated probability mass inside the AFR must be consistent with the number of normal data points inside the AFR. Based on this insight, we provide a solid theoretical foundation and a reference implementation of anomaly detection using AFRs. Our empirical results confirm that anomaly detection constrained via AFRs improves upon unconstrained anomaly detection. Specifically, we show that, when equipped with an estimated AFR, an efficient algorithm based on random guessing becomes a strong baseline that several widely-used methods struggle to overcome. On a dataset with a ground-truth AFR available, the current state of the art is outperformed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。