用可调度噪声提升表格数据异常检测效果
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
- 引入扩散模型的噪声调度机制，动态调整去噪强度
- 在57个数据集上，半监督下PR-AUC提升65%，无监督下ROC-AUC提升16%
- 适合处理标注少、异常样本稀缺的表格数据场景
表格数据中的异常检测因特征间复杂交互和异常样本稀缺而困难。传统去噪自编码器使用固定强度噪声，难以适应多样数据分布；扩散模型虽具可调度噪声与迭代去噪能力，但缺乏显式重建映射。本文提出扩散调度去噪自编码器（DDAE），将扩散式噪声调度与对比学习融入编码过程，增强异常检测性能。在ADBench的57个数据集上评估显示，该方法在半监督设置中表现更优，相较最优基线模型，PR-AUC最高提升65%（平均9%），ROC-AUC提升16%（平均6%）。研究发现：无监督训练时高噪声水平更有效，半监督设置下低噪声与线性调度最优。结果表明，合理的噪声策略对表格异常检测至关重要。
原文摘要 · Abstract (English)
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.
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