Anomaly detection is a crucial task in various domains. Most of the existing methods assume the normal sample data clusters around a single central prototype while the real data may consist of multiple categories or subgroups. In addition, existing methods always assume all unlabeled samples are normal while some of them are inevitably being anomalies. To address these issues, we propose a novel anomaly detection framework that can efficiently work with limited labeled anomalies. Specifically, we assume the normal sample data may consist of multiple subgroups, and propose to learn multi-normal prototypes to represent them with deep embedding clustering and contrastive learning. Additionally, we propose a method to estimate the likelihood of each unlabeled sample being normal during model training, which can help to learn more efficient data encoder and normal prototypes for anomaly detection. Extensive experiments on various datasets demonstrate the superior performance of our method compared to state-of-the-art methods.

翻译：异常检测是各领域中的关键任务。现有方法大多假设正常样本数据围绕单一中心原型聚类，而实际数据可能包含多个类别或子群。此外，现有方法通常假设所有未标注样本均为正常样本，但其中不可避免地存在异常样本。为解决这些问题，我们提出一种新型异常检测框架，能够在有限标注异常样本条件下高效工作。具体而言，我们假设正常样本数据可能包含多个子群，并提出通过深度嵌入聚类与对比学习来构建多正常原型以表征这些子群。此外，我们提出一种在模型训练过程中估计未标注样本正常概率的方法，该方法有助于学习更高效的数据编码器与正常原型以进行异常检测。在多组数据集上的大量实验表明，相较于现有最优方法，本方法具有更优越的性能。