用于弱监督异常检测的多正常原型学习
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
AI总结：
针对正常数据存在多个子群且未标记样本含异常的问题，提出结合深度嵌入聚类、对比学习和未标记样本正常可能性估计的多正常原型弱监督异常检测框架，并在多数据集上验证其优越性能。
摘要：
异常检测是众多领域中的一项关键任务。大多数现有方法假设正常样本数据围绕单一中心原型聚集，但真实数据可能由多个类别或子群组成。此外，现有方法总是假设所有未标记样本都是正常的，而其中不可避免地会有一些异常样本。为解决这些问题，我们提出了一种新颖的异常检测框架，能够在仅有少量标记异常的情况下高效工作。具体而言，我们假设正常样本数据可能由多个子群组成，并提出通过深度嵌入聚类和对比学习来学习多个正常原型以表示这些子群。此外，我们提出了一种在模型训练期间估计每个未标记样本为正常样本的可能性的方法，这有助于学习更高效的数据编码器和用于异常检测的正常原型。在多个数据集上进行的大量实验表明，与最先进的方法相比，我们的方法具有优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
