IDEAL learns intrinsic deviation vectors from normal and anomalous references via a Normal Variation Eraser and Intrinsic Deviation Encoder to score query deviations and generalize to unseen anomalies on eight datasets.
arXiv preprint arXiv:2108.00462 , year=
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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.
Under cold-start scarcity, ArcAD's Sinkhorn-balanced hyperspherical clustering plus anomaly-guided repulsion lifts reconstruction-based anomaly detection, with the clearest gains (+3.7 to +11.2 I-AUROC) on large multi-class benchmarks.
Anomaly Preference Optimization reformulates anomaly image generation as preference learning using real anomalies for implicit alignment signals from denoising trajectories plus a time-aware capacity allocation module.
DPDL learns multiple Gaussian prototypes and a Schrödinger bridge diffusion process to enclose normal samples in a compact discriminative space while using hyperspherical dispersion to identify out-of-distribution anomalies, reporting SOTA results on 9 datasets.
citing papers explorer
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Beyond Normal References: Discriminative Few-Shot Anomaly Detection
IDEAL learns intrinsic deviation vectors from normal and anomalous references via a Normal Variation Eraser and Intrinsic Deviation Encoder to score query deviations and generalize to unseen anomalies on eight datasets.
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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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ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
Under cold-start scarcity, ArcAD's Sinkhorn-balanced hyperspherical clustering plus anomaly-guided repulsion lifts reconstruction-based anomaly detection, with the clearest gains (+3.7 to +11.2 I-AUROC) on large multi-class benchmarks.
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Anomaly-Preference Image Generation
Anomaly Preference Optimization reformulates anomaly image generation as preference learning using real anomalies for implicit alignment signals from denoising trajectories plus a time-aware capacity allocation module.
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Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection
DPDL learns multiple Gaussian prototypes and a Schrödinger bridge diffusion process to enclose normal samples in a compact discriminative space while using hyperspherical dispersion to identify out-of-distribution anomalies, reporting SOTA results on 9 datasets.