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.

翻译：在许多现实场景中，获取大量标注数据往往是一项艰巨的任务。近年来，弱监督学习技术作为传统监督学习的替代方案受到了广泛关注，它仅需少量标注数据即可训练模型。本文针对缺陷检测任务，将弱监督分类器与其全监督对应模型的性能进行了比较。实验基于包含缺陷的图像数据集开展，并根据准确率、精确率和召回率对两种分类器进行了评估。结果表明，弱监督分类器在显著减少标注数据需求的同时，取得了与监督分类器相当的性能。