Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.

翻译：半监督学习因其利用大量未标记数据增强模型鲁棒性的潜力而受到广泛关注。伪标签是半监督学习中广泛采用的策略。然而，现有方法常受噪声污染困扰，可能损害模型性能。为应对这一挑战，我们提出了一种新颖的协同引导区域伪标签监督（SGRS-Net）框架。该框架基于均值教师网络构建，我们采用混合增强模块来增强未标记数据。通过评估增强前后的协同性，我们策略性地将伪标签划分为不同区域。此外，我们引入了区域损失评估模块，以评估每个划定区域的损失。在LA数据集上进行的大量实验表明，本框架性能优于现有先进技术，凸显了其高效性与实用性。