面向表格数据异常检测的扩散调度去噪自编码器
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
AI总结：
针对表格数据异常检测中现有去噪自编码器适应性不足、扩散模型缺显式重构映射的问题，提出整合扩散噪声调度与对比学习的DDAE框架，在ADBench的57个数据集上取得优于现有基线的效果。
摘要：
表格数据中的异常检测因特征交互复杂、异常样本稀缺而仍具挑战性。去噪自编码器依赖固定幅度的噪声，限制了其对不同数据分布的适应性。扩散模型引入了调度噪声与迭代去噪机制，但缺乏显式的重构映射。我们提出扩散调度去噪自编码器（DDAE），该框架将基于扩散的噪声调度与对比学习整合到编码过程中，以提升异常检测效果。我们在来自ADBench的57个数据集上评估了DDAE。我们的方法在半监督设置下表现更优，在无监督设置下取得了具有竞争力的结果，相较于最先进的自编码器（扩散）模型基线，PR-AUC最高提升65%（9%），ROC-AUC提升16%（6%）。我们观察到更高的噪声水平有利于无监督训练，而带线性调度的更低噪声在半监督设置下是最优的。这些发现凸显了系统化噪声策略在表格异常检测中的重要性。
英文摘要：
Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noise, limiting adaptability to diverse data distributions. Diffusion models introduce scheduled noise and iterative denoising, but lack explicit reconstruction mappings. We propose the Diffusion-Scheduled Denoising Autoencoder (DDAE), a framework that integrates diffusion-based noise scheduling and contrastive learning into the encoding process to improve anomaly detection. We evaluated DDAE on 57 datasets from ADBench. Our method outperforms in semi-supervised settings and achieves competitive results in unsupervised settings, improving PR-AUC by up to 65% (9%) and ROC-AUC by 16% (6%) over state-of-the-art autoencoder (diffusion) model baselines. We observed that higher noise levels benefit unsupervised training, while lower noise with linear scheduling is optimal in semi-supervised settings. These findings underscore the importance of principled noise strategies in tabular anomaly detection.
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。
