Mixed supervision for surface-defect detectionBožič, Jakob (Avtor) Skočaj, Danijel (Mentor) Tabernik, Domen (Komentor) deep learningsurface-defect detectionmixed supervisionanomaly detectionrobustnessSurface-defect detection aims to identify defective regions in images. Recently, various deep-learning based solutions have been proposed to tackle this task, requiring different types of labels for training data. Fully supervised approaches require costly-to-produce pixel-level labels for all samples, but deliver excellent performance. On the other end of the spectrum, unsupervised methods are trained from normal data only, but often fall short in performance. Neither of these are capable of utilizing all the available data, as the first can not employ weakly labeled data and the latter fail to consider defective samples. We introduce mixed supervision to bridge the performance gap between fully supervised and unsupervised methods by enabling learning from all available data. A fully supervised method that can learn from weakly-labeled data is proposed and an unsupervised method is extended to extract knowledge from defective samples. Extensive evaluation of mixed supervision shows that allowing both methods to learn from previously unused data significantly improves their performance. In a detailed analysis of robustness, unsupervised methods prove to be surprisingly robust to false negatives in the training data, showing potential for use in fully unsupervised scenario with completely unlabeled data.20222022-09-27 08:05:00Magistrsko delo/naloga141253sl