Image Models#
Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization
Real-Time Unsupervised Anomaly Detection via Conditional Normalizing Flows
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
Deep Feature Kernel Density Estimation
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection
DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
FRE: A Fast Method For Anomaly Detection And Segmentation
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
Towards Total Recall in Industrial Anomaly Detection
Anomaly Detection via Reverse Distillation from One-Class Embedding.
Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
VLM-AD: Vision-Language Model for Anomaly Detection
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation