用混合高斯原型流匹配，让异常检测更准地分辨未见异常。
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
- 构建多模态高斯混合原型空间，替代传统单模高斯建模。
- 在多个数据集上达到当前最优，单/多异常场景均有效。
- 适合需要精准识别未知异常的工业检测任务。
开放集监督异常检测（OSAD）旨在利用有限的异常标注识别未见过的异常。现有基于原型的方法通常采用单峰高斯先验建模正常数据，难以捕捉内在多模态特性，导致决策边界模糊。为此，本文提出混合原型流匹配（MPFM）框架，学习从正常特征分布到结构化高斯混合原型空间的连续变换。与依赖单一速度向量的传统流方法不同，MPFM显式将速度场建模为高斯混合先验，每个分量对应一个独立的正常类别，实现模式感知且语义连贯的分布传输。此外，引入互信息最大化正则化器（MIMR），防止原型坍缩并增强正常与异常的可分性。大量实验表明，MPFM在多种基准上均取得当前最优性能，适用于单异常与多异常场景。
原文摘要 · Abstract (English)
Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, failing to capture inherent multi-modality and resulting in blurred decision boundaries. To address this, we propose Mixture Prototype Flow Matching (MPFM), a framework that learns a continuous transformation from normal feature distributions to a structured Gaussian mixture prototype space. Departing from traditional flow-based approaches that rely on a single velocity vector, MPFM explicitly models the velocity field as a Gaussian mixture prior where each component corresponds to a distinct normal class. This design facilitates mode-aware and semantically coherent distribution transport. Furthermore, we introduce a Mutual Information Maximization Regularizer (MIMR) to prevent prototype collapse and maximize normal-anomaly separability. Extensive experiments demonstrate that MPFM achieves state-of-the-art performance across diverse benchmarks under both single- and multi-anomaly settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。