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