Defect detection using weakly supervised learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909233334714368 |
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| author | Sevetlidis, Vasileios Pavlidis, George Balaska, Vasiliki Psomoulis, Athanasios Mouroutsos, Spyridon Gasteratos, Antonios |
| author_facet | Sevetlidis, Vasileios Pavlidis, George Balaska, Vasiliki Psomoulis, Athanasios Mouroutsos, Spyridon Gasteratos, Antonios |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2303_15092 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Defect detection using weakly supervised learning Sevetlidis, Vasileios Pavlidis, George Balaska, Vasiliki Psomoulis, Athanasios Mouroutsos, Spyridon Gasteratos, Antonios Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Defect detection using weakly supervised learning |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2303.15092 |