MPFM models flow matching velocity as a Gaussian mixture prior per normal class plus a mutual information regularizer to improve open-set anomaly detection over unimodal prototypes.
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
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Flow Mismatching detects anomalies via aggregated velocity mismatches along noise-to-image paths in a flow matching model trained only on normal data, yielding pixel heatmaps without reconstruction or test-time optimization.
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Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
MPFM models flow matching velocity as a Gaussian mixture prior per normal class plus a mutual information regularizer to improve open-set anomaly detection over unimodal prototypes.
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Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models
Flow Mismatching detects anomalies via aggregated velocity mismatches along noise-to-image paths in a flow matching model trained only on normal data, yielding pixel heatmaps without reconstruction or test-time optimization.