FPFNet reports state-of-the-art AUROC scores on MVTec-AD and VisA for unified multi-class defect detection by adding feature perturbation and hierarchical fusion to UniAD with no extra parameters.
Explaining deep neural networks and beyond: A review of methods and applications.Proceedings of the IEEE, 109(3):247–278
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Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection
FPFNet reports state-of-the-art AUROC scores on MVTec-AD and VisA for unified multi-class defect detection by adding feature perturbation and hierarchical fusion to UniAD with no extra parameters.