Self-Supervised Iterative Refinement for Anomaly Detection in Industrial
Quality Control
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial
Quality Control
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness using two benchmark …