Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913128510390272 |
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| author | Wang, Fuyun Wang, Yuanzhi Guo, Xu Huang, Sujia Zhang, Tong Wang, Dan Yan, Hui Liu, Xin Cui, Zhen |
| author_facet | Wang, Fuyun Wang, Yuanzhi Guo, Xu Huang, Sujia Zhang, Tong Wang, Dan Yan, Hui Liu, Xin Cui, Zhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02438 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection Wang, Fuyun Wang, Yuanzhi Guo, Xu Huang, Sujia Zhang, Tong Wang, Dan Yan, Hui Liu, Xin Cui, Zhen Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.02438 |