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=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.CV 5verdicts
UNVERDICTED 5representative citing papers
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.
ArcAD is a plug-and-play push-pull calibration method that projects limited normal samples onto a hypersphere for compact clustering while synthesizing pseudo-anomalies and using real anomalies to refine the decision boundary for reconstruction-based IAD under data scarcity.
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
-
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.
-
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.
-
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
ArcAD is a plug-and-play push-pull calibration method that projects limited normal samples onto a hypersphere for compact clustering while synthesizing pseudo-anomalies and using real anomalies to refine the decision boundary for reconstruction-based IAD under data scarcity.
-
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.
-
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.