Uses DDPM to synthesize defects via Gaussian noise and Perlin masks, then asymmetric teacher-student network for 98.4% image AUROC and 98.3% pixel AUROC on MVTecAD in unsupervised setting.
Image-based surface defect detection using deep learning: A review[J]
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Industrial Surface Defect Detection via Diffusion Generation and Asymmetric Student-Teacher Network
Uses DDPM to synthesize defects via Gaussian noise and Perlin masks, then asymmetric teacher-student network for 98.4% image AUROC and 98.3% pixel AUROC on MVTecAD in unsupervised setting.