无监督学习正常数据模式，用代理模型检测异常。
Unsupervised Surrogate Anomaly Detection
- 构建代理模型捕捉正常数据规律
- 在121个数据集上表现优于19种现有方法
- 适合需要可靠异常检测的工业场景
本文研究无监督异常检测算法，通过神经网络学习正常数据的规律性表征，异常即偏离这些规律。受工程领域类似概念启发，提出代理异常检测方法。我们形式化了最优代理模型所需的公理，并设计新算法DEAN（Deep Ensemble ANomaly detection）以满足这些条件。在121个基准数据集上评估，结果表明DEAN性能优于19种现有方法，同时展现出良好的可扩展性与可靠性。
原文摘要 · Abstract (English)
In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating from. Inspired by a similar concept in engineering, we refer to our methodology as surrogate anomaly detection. We formalize the concept of surrogate anomaly detection into a set of axioms required for optimal surrogate models and propose a new algorithm, named DEAN (Deep Ensemble ANomaly detection), designed to fulfill these criteria. We evaluate DEAN on 121 benchmark datasets, demonstrating its competitive performance against 19 existing methods, as well as the scalability and reliability of our method.
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