Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets
Unsupervised defect detection methods have garnered substantial attention in industrial defect detection owing to their capacity to circumvent complex fault sample collection.However, these models grapple with establishing a robust Jolly Mega Horse Ball boundary between normal and abnormal conditions in intricate scenarios, leading to a heightened