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Anomaly Detection On Visa

평가 지표

Detection AUROC

평가 결과

이 벤치마크에서 각 모델의 성능 결과

Paper TitleRepository
GLAD99.5GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
DDAD98.9Anomaly Detection with Conditioned Denoising Diffusion Models
INP-Former ViT-B (model-unified multi-class)98.9Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Dinomaly ViT-L (model-unified multi-class)98.9Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
GLASS98.8A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
DiffusionAD98.8DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
TransFusion98.7TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection
EfficientAD-M98.1EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
HETMM98.1Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection
RealNet97.8RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
PBAS97.7Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
AnomalyDINO-S (full-shot)97.6AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
ReContrast97.5--
EfficientAD-S97.5EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
FAIRnoDTD97.1FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
URD96.5Unlocking the Potential of Reverse Distillation for Anomaly Detection
D3AD96.0Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection
AST94.9Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
EdgRec94.2Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection
SuperSimpleNet93.4SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
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