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Anomaly Detection On Mpdd

评估指标

Detection AUROC
Segmentation AUROC

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
GLASS99.699.4A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
DMDD98.1098.96Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection-
PBAS97.798.8Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
GLAD97.598.7GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
POUTA97.5-Produce Once, Utilize Twice for Anomaly Detection-
Dinomaly97.299.1Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
RealNet96.398.2RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
DiffusionAD96.298.5DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
ULSAD95.7397.45Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
LeMO87.497.8Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision-
AdaCLIP82.596.1AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
FastRecon82.597.9FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction-
PatchCore82.1295.66Towards Total Recall in Industrial Anomaly Detection
KAnoCLIP77.898.3KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration-
ADSPR-96.8Anomaly Detection using Score-based Perturbation Resilience-
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Anomaly Detection On Mpdd | SOTA | HyperAI超神经