Defect detection using weakly supervised learning
This addresses the problem of limited labeled data for defect detection in industrial or quality control settings, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackled defect detection by comparing weakly supervised and fully supervised classifiers, finding that the weakly supervised approach achieved comparable performance in accuracy, precision, and recall while using significantly less labeled data.
In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data.