通过伪标签引导生成增强表格异常检测
Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
AI总结：
本文提出PLAG方法，通过伪标签引导生成提升表格异常检测性能，结合双阶段数据选择策略，有效过滤候选样本，增强模型对局部异常信号的识别能力。
摘要：
识别表格数据中的异常实例对于提高数据可靠性及维持系统稳定性至关重要。由于真实异常标签稀缺，现有方法主要依赖无监督异常检测模型或利用少量标注异常实例通过样本生成或对比学习来辅助检测。然而，无监督方法缺乏足够的异常意识，而当前生成和对比方法倾向于全局计算异常，忽视了表格特征的局部异常模式，导致检测性能不佳。为解决这些问题，我们提出PLAG，一种伪标签引导的异常生成方法，旨在增强表格异常检测。具体而言，通过利用伪异常作为指导信号，并将样本的整体异常量化分解为特征层面的累积异常，PLAG不仅有效消除了对稀缺真实标签的需求，还为模型提供了理解细粒度局部异常信号的新视角。此外，提出了一种双阶段数据选择策略，整合格式验证和不确定性估计，严格过滤候选样本，从而确保合成异常的一致性和多样性。最终，这些过滤后的合成异常作为稳健的判别引导，使模型能够更好地分离正常和异常实例。大量实验表明，PLAG在八个代表性基线中实现了最先进的性能。此外，作为灵活的框架，它能够无缝集成到现有无监督检测器中，一致地提升F1分数由0.08到0.21。
英文摘要：
Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning. However, unsupervised methods lack sufficient anomaly awareness, while current generation and contrastive approaches tend to compute anomalies globally, overlooking the localized anomaly patterns of tabular features, resulting in suboptimal detection performance. To address these limitations, we propose PLAG, a pseudo-label-guided anomaly generation method designed to enhance tabular anomaly detection. Specifically, by utilizing pseudo-anomalies as guidance signals and decoupling the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities, PLAG not only effectively obviates the need for scarce ground-truth labels but also provides a novel perspective for the model to comprehend localized anomalous signals at a fine-grained level. Furthermore, a two-stage data selection strategy is proposed, integrating format verification and uncertainty estimation to rigorously filter candidate samples, thereby ensuring the fidelity and diversity of the synthetic anomalies. Ultimately, these filtered synthetic anomalies serve as robust discriminative guidance, empowering the model to better separate normal and anomalous instances. Extensive experiments demonstrate that PLAG achieves state-of-the-art performance against eight representative baselines. Moreover, as a flexible framework, it integrates seamlessly with existing unsupervised detectors, consistently boosting F1-scores by 0.08 to 0.21.
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。
