Abstract
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.
Abstract (translated)
半监督学习因其能够利用丰富的未标记数据来增强模型的鲁棒性而受到了广泛关注。伪标签是一种在半监督学习中广泛使用的技术策略。然而，现有的方法经常受到噪声污染的影响，这可能会削弱模型的表现。为了解决这个挑战，我们提出了一种新的协同引导区域化伪标签监督（SGRS-Net）框架。该框架基于平均教师网络构建，并采用混合增强模块来提升未标记数据的质量。通过评估增强前后的一致性，我们将伪标签战略性地划分为不同的区域。此外，我们引入了一个区域损失评估模块，用于评价每个划分区域内各自的损失情况。在LA数据集上进行的大量实验表明，我们的框架表现优于最先进的技术，这突显了该框架的有效性和实用性。
URL
https://arxiv.org/abs/2411.04493
