A knowledge-guided loss enforces saliency map consistency between a primary and final model in multi-task learning to boost accuracy, AP, and interpretability in defect detection.
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection.CIRP annals, 65(1):417–420
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Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
A knowledge-guided loss enforces saliency map consistency between a primary and final model in multi-task learning to boost accuracy, AP, and interpretability in defect detection.