Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection

ArXi:2605.17780v1 Announce Type: new Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead to performance bottlenecks and limited trustworthiness in real-world applications. To address these challenges, this paper proposes a novel knowledge-guided loss function that seamlessly integrates model interpretability into the