基于单步流匹配的可扩展、可解释且可证明鲁棒的异常检测
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
AI总结：
针对表格数据半监督异常检测，提出基于流匹配思想的TCCM方法，兼具可扩展性、可解释性与鲁棒性，在ADBench基准上实现精度与推理成本的更佳平衡。
摘要：
我们提出了时间条件收缩匹配（Time-Conditioned Contraction Matching，TCCM），一种用于表格数据半监督异常检测的新方法。TCCM的灵感来源于流匹配（flow matching）——一种新兴的生成建模框架，用于学习概率分布之间的速度场，且相比扩散模型和生成对抗网络已展现出优异性能。\nTCCM并未直接套用流匹配的原始公式，而是基于其学习分布间速度场的核心思想，通过在每个采样时间步预测指向固定目标（原点）的时间条件收缩向量简化了该框架。这一设计具备三大核心优势：（1）轻量且可扩展的训练目标，训练和推理阶段均无需求解常微分方程；（2）名为单时间步偏差的高效评分策略，可通过单次前向传播量化与预期收缩行为的偏差，解决了现有连续时间模型的推理瓶颈——例如DTE（一种基于扩散的模型，拥有领先的异常检测精度，但推理成本高昂）；（3）可解释性与可证明鲁棒性，由于学习到的速度场直接在输入空间中运行，异常分数天然具备逐特征归因能力；此外，评分函数关于输入满足Lipschitz连续，为微小扰动下的性能提供了理论保证。\n在ADBench基准上的大量实验表明，TCCM在检测精度与推理成本之间实现了良好平衡，性能优于现有最优方法，尤其在高维、大规模数据集上表现突出。源代码已发布于我们的GitHub仓库。
英文摘要：
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generative modeling framework that learns velocity fields between probability distributions and has shown strong performance compared to diffusion models and generative adversarial networks. Instead of directly applying flow matching as originally formulated, TCCM builds on its core idea -- learning velocity fields between distributions -- but simplifies the framework by predicting a time-conditioned contraction vector toward a fixed target (the origin) at each sampled time step. This design offers three key advantages: (1) a lightweight and scalable training objective that removes the need for solving ordinary differential equations during training and inference; (2) an efficient scoring strategy called one time-step deviation, which quantifies deviation from expected contraction behavior in a single forward pass, addressing the inference bottleneck of existing continuous-time models such as DTE (a diffusion-based model with leading anomaly detection accuracy but heavy inference cost); and (3) explainability and provable robustness, as the learned velocity field operates directly in input space, making the anomaly score inherently feature-wise attributable; moreover, the score function is Lipschitz-continuous with respect to the input, providing theoretical guarantees under small perturbations. Extensive experiments on the ADBench benchmark show that TCCM strikes a favorable balance between detection accuracy and inference cost, outperforming state-of-the-art methods -- especially on high-dimensional and large-scale datasets. The source code is available at our GitHub repository.
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。
