用扩散模型分析数据分布偏移类型，让系统能区分不同异常数据
Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
- 通过扩散模型多阶段去噪过程提取多统计特征向量
- 在图像与表格数据上既保持检测性能，又可分类异常类型
- 适合需要精准判断异常来源的高风险应用
检测分布外（OOD）数据对机器学习的安全性和开放学习至关重要。然而，现有方法将分布偏移简化为单一标量分数，无法区分异常类型，限制了后续决策。本文提出DISC：基于扩散模型的统计特征表征方法，利用扩散模型迭代去噪过程，在多个噪声水平下提取多维特征向量，以捕捉统计差异。在图像与表格基准测试上的大量实验表明，DISC在OOD检测性能上达到或超过当前最优方法，并首次实现对OOD类型的分类能力，显著超越以往仅做二元判断的方法。本工作推动了从简单二分类走向细粒度分布偏移识别的转变。
原文摘要 · Abstract (English)
Detecting out-of-distribution (OOD) data is critical for machine learning, be it for safety reasons or to enable open-ended learning. However, beyond mere detection, choosing an appropriate course of action typically hinges on the type of OOD data encountered. Unfortunately, the latter is generally not distinguished in practice, as modern OOD detection methods collapse distributional shifts into single scalar outlier scores. This work argues that scalar-based methods are thus insufficient for OOD data to be properly contextualized and prospectively exploited, a limitation we overcome with the introduction of DISC: Diffusion-based Statistical Characterization. DISC leverages the iterative denoising process of diffusion models to extract a rich, multi-dimensional feature vector that captures statistical discrepancies across multiple noise levels. Extensive experiments on image and tabular benchmarks show that DISC matches or surpasses state-of-the-art detectors for OOD detection and, crucially, also classifies OOD type, a capability largely absent from prior work. As such, our work enables a shift from simple binary OOD detection to a more granular detection.
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