Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. In this paper, we propose to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end, we introduce a novel approach, namely Anomaly Heterogeneity Learning (AHL), that simulates a diverse set of heterogeneous (seen and unseen) anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model. Further, AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art (SOTA) OSAD models in detecting both seen and unseen anomalies, achieving new SOTA performance on a large set of datasets, and 2) effectively generalize to unseen anomalies in new target domains.

翻译：开放集监督异常检测（OSAD）——一个新兴的异常检测领域——旨在利用训练中见过的少量异常类别样本来检测未见异常（即来自开放集异常类别的样本），同时有效识别已见异常。得益于已见异常所体现的先验知识，当前的OSAD方法通常能大幅降低误报错误。然而，这些方法将异常样本视为来自同质分布，导致其在泛化至可能来自任意分布的未见异常时效果欠佳。本文中，我们提出利用有限的异常样本来学习异质异常分布以解决该问题。为此，我们引入了一种新颖方法，即异常异质性学习（AHL），该方法模拟一组多样化的异质（已见与未见）异常分布，进而利用它们学习统一的异质异常模型。此外，AHL是一个通用框架，现有OSAD模型可即插即用地增强其异常建模能力。在九个真实世界异常检测数据集上的大量实验表明，AHL能够：1）显著增强不同最先进（SOTA）OSAD模型在检测已见和未见异常时的性能，在大量数据集上实现新的SOTA表现；2）有效泛化至新目标域中的未见异常。