Liu Yuxuan, Wen Chenglin
School of Automation, Guangdong University of Petrochemical Technology, Maoming, 510006, China.
Sci Rep. 2025 Jul 1;15(1):22022. doi: 10.1038/s41598-025-02324-0.
Semi-supervised learning mitigates the problem of labeled data scarcity by utilizing unlabeled data, but the generalization performance of existing methods usually degrades significantly when the unlabeled data is small in size or poor in quality. To this end, this paper proposes a semi-supervised image classification method based on multi-mode augmentation, which mitigates the effects of insufficient quality and limited scale of unlabeled data by simultaneously improving the sample completeness within and between classes. Specifically, the model's prediction confidence and bias are used for uncertainty-based screening to improve pseudo-label quality, while retaining as many unlabeled samples as possible to fully exploit their potential information. Secondly, a multi-modal data augmentation strategy combining intra-class random augmentation and inter-class mixed augmentation is designed to enhance the diversity of the data and the feature expression capability. Finally, a pseudo-label consistency metric is introduced to further improve the model's generalization ability. The experimental results on STL-10 and CIFAR-10 datasets show that the generalization performance of the proposed method is significantly better than the existing mainstream methods in the scenarios of small unlabeled data and mismatched samples.
半监督学习通过利用未标记数据缓解了标记数据稀缺的问题，但当未标记数据规模较小或质量较差时，现有方法的泛化性能通常会显著下降。为此，本文提出了一种基于多模态增强的半监督图像分类方法，该方法通过同时提高类内和类间的样本完整性来减轻未标记数据质量不足和规模有限的影响。具体来说，利用模型的预测置信度和偏差进行基于不确定性的筛选，以提高伪标签质量，同时保留尽可能多的未标记样本以充分挖掘其潜在信息。其次，设计了一种结合类内随机增强和类间混合增强的多模态数据增强策略，以增强数据的多样性和特征表达能力。最后，引入了伪标签一致性度量以进一步提高模型的泛化能力。在STL-10和CIFAR-10数据集上的实验结果表明，在未标记数据较少和样本不匹配的场景中，所提方法的泛化性能明显优于现有主流方法。
