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Anomaly Detection On Mpdd

Metriken

Detection AUROC

Segmentation AUROC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

			Paper Title	Repository
GLASS	99.6	99.4	A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
DMDD	98.10	98.96	Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection	-
PBAS	97.7	98.8	Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
GLAD	97.5	98.7	GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
POUTA	97.5	-	Produce Once, Utilize Twice for Anomaly Detection	-
Dinomaly	97.2	99.1	Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
RealNet	96.3	98.2	RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
DiffusionAD	96.2	98.5	DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
ULSAD	95.73	97.45	Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
LeMO	87.4	97.8	Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision	-
AdaCLIP	82.5	96.1	AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
FastRecon	82.5	97.9	FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction	-
PatchCore	82.12	95.66	Towards Total Recall in Industrial Anomaly Detection
KAnoCLIP	77.8	98.3	KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration	-
ADSPR	-	96.8	Anomaly Detection using Score-based Perturbation Resilience	-

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