检测不可检测：通过主动学习增强无监督时间序列异常检测
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
AI总结：
提出一种结合主动学习的框架，通过掩码重建反馈和极小极大学习策略，增强无监督模型对细微噪声异常的检测能力，在28个测试案例中AUC提升12.39%。
摘要：
尽管工业AI系统日益复杂，但在复杂时间序列数据中可靠检测细微和噪声异常仍然是一个关键但未解决的挑战。在大规模工业应用中，标记时间序列数据通常成本高昂且耗时，使得无监督学习成为一种实用且广泛采用的方法。然而，现有的无监督方法常常难以区分接近正常的异常与正常模式，并且容易受到正常样本中噪声污染的影响。为了解决这些局限性，我们提出了一种新颖的框架，利用主动学习迭代地增强无监督模型的性能。我们框架的核心贡献是：(1) 一种掩码时间序列重建反馈策略，迫使模型学习鲁棒的时序依赖关系；(2) 一种极小极大学习策略，通过差异化处理正常和异常样本来促进鲁棒性。这一过程鼓励模型更好地捕捉细微和噪声模式的动态。该框架在涉及四个多变量时间序列数据集和七个无监督骨干模型的28个测试案例中进行了评估。实验结果表明，与原始模型相比，AUC提升了12.39%，证实了我们的方法可以轻松集成到现有的基于重建的无监督异常检测系统中，显著提升其性能。
英文摘要：
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge. In large-scale industrial applications, labeling time series data is often prohibitively expensive and time-consuming, making unsupervised learning a practical and widely adopted approach. However, existing unsupervised methods frequently struggle to distinguish near-normal anomalies from normal patterns and are vulnerable to noise contamination within normal samples. To address these limitations, we propose a novel framework that leverages active learning to iteratively enhance the performance of unsupervised models. Our framework's core contributions are (1) a masked time-series reconstruction feedback strategy that forces the model to learn robust temporal dependencies, and (2) a minimax learning strategy that promotes robustness by differentially treating normal and abnormal samples. This process encourages the model to better capture the dynamics of subtle and noisy patterns. The proposed framework is evaluated across 28 test cases involving four multivariate time-series datasets and seven unsupervised backbone models. Experimental results demonstrate a 12.39% improvement in AUC compared to the original models, confirming that our method can be readily integrated into existing unsupervised reconstruction-based anomaly detection systems to significantly enhance their performance.
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。
