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SOTA
異常検出
Anomaly Detection On Btad
Anomaly Detection On Btad
評価指標
Detection AUROC
Segmentation AUPRO
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Detection AUROC
Segmentation AUPRO
Paper Title
Repository
ReConPatch WRN-50
95.8
97.5
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
PatchSVDD
-
-
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
CPR
94.8
85.1
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
MuSc (zero-shot)
96.16
83.43
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
RealNet
96.1
-
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
FastFlow+AltUB
-
-
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection
-
PNI
-
-
PNI : Industrial Anomaly Detection using Position and Neighborhood Information
URD
93.9
78.5
Unlocking the Potential of Reverse Distillation for Anomaly Detection
AD-CLSCNFs
95.93
72.77
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification
-
WeakREST-Un
94.4
84.9
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
-
VT-ADL
-
-
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
D3AD
95.2
83.2
Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection
Reverse Distillation ++
95.63
-
Revisiting Reverse Distillation for Anomaly Detection
-
PyramidFlow (Res18)
95.8
-
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow
-
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