Defect detection using weakly supervised learning

• URL: http://arxiv.org/abs/2303.15092v1
• Date: Mon, 27 Mar 2023 11:01:16 GMT
• Title: Defect detection using weakly supervised learning
• Authors: Vasileios Sevetlidis and George Pavlidis and Vasiliki Balaska and Athanasios Psomoulis and Spyridon Mouroutsos and Antonios Gasteratos
• Abstract summary: Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection.
• Score: 1.4321190258774352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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