Search Results for author:
Found 3 papers, 2 papers with code
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD.
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations.
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively.
Ranked #3 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
