重新审视基于能量的表格型异常检测：能量与重构是互补的
Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary
- Meijo University（名城大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出经典深度玻尔兹曼机（DBM）作为表格型异常检测的互补能量模型，实验显示其与自编码器融合后在两个基准数据集上性能显著提升，为该领域提供了新的有效工具。
AI中文摘要：
表格型异常检测目前主要由经典的密度代理方法（孤立森林Isolation Forest、一类支持向量机OCSVM、局部异常因子LOF）、基于重构的检测器（自编码器Autoencoders、变分自编码器VAEs）以及现代非参数评分器（COPOD、ECOD、Deep SVDD）主导，所有这些方法都仅间接近似内点分布；显式基于能量的模型（EBM）在该领域基本缺失。受深度学习中EBM近期复兴（如基于能量的Transformer、联合预测与嵌入架构JEPA）的启发，本文重新审视经典的深度玻尔兹曼机（DBM）在该任务中的应用，并假设其平均场能量与基于重构的分数的结合效果优于同谱系方法的结合效果。我们在两个跨不同领域的表格型基准数据集（UCI银行营销数据集和NSL-KDD数据集）上，针对8种经典及现代基线方法，在20个随机种子下评估了双隐藏层DBM的性能。DBM平均场能量在银行营销数据集上与最强基线（自编码器）表现相当，在NSL-KDD数据集上统计上优于该基线，且在两个数据集上均显著优于其余7种基线。当通过秩融合将DBM能量与自编码器融合时，在两个数据集上均取得了统计显著的改进（银行营销数据集AUROC提升0.014，p值<0.01；NSL-KDD数据集AUROC提升0.002，p值<0.001）；而所有非DBM衍生的基础模型与自编码器配对的集成则无法改进或显著降低性能。本文的观点是，以DBM为代表的经典EBM应在表格型异常检测工具包中占据一席之地，作为当前占主导地位的基于重构分数的非冗余互补视角。
英文摘要：
Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent. Motivated by the recent revival of EBMs in deep learning (e.g., Energy-Based Transformers, JEPA), we revisit the classical Deep Boltzmann Machine (DBM) for this task and hypothesize that its mean-field energy combines more effectively with a reconstruction-based score than same-lineage pairs do. We evaluate a two-hidden-layer DBM on two tabular benchmarks spanning distinct domains (UCI Bank Marketing and NSL-KDD) against eight classical and modern baselines across twenty random seeds. The DBM mean-field energy matches the strongest baseline (the Autoencoder) on Bank Marketing and statistically beats it on NSL-KDD, while significantly outperforming the remaining seven on both datasets. When fused with the Autoencoder via rank fusion, the DBM energy yields a statistically significant improvement on both datasets (AUROC=+0.014, p<0.01 on Bank Marketing; +0.002, p<0.001 on NSL-KDD); every non-DBM-derived base model instead fails to improve or significantly degrades the AE-paired ensemble. Our position is that classical EBMs, exemplified by the DBM, deserve a place in the tabular anomaly detection toolbox as a non-redundant complementary view to the reconstruction-based scores that dominate current practice.
