Semi-supervised learning provides an expressive framework for exploiting unlabeled data when labels are insufficient. Previous semi-supervised learning methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning not robust. In this paper, we propose a novel SSL method called DualMatch, in which the class prediction jointly invokes feature embedding in a dual-level interaction manner. DualMatch requires consistent regularizations for data augmentation, specifically, 1) ensuring that different augmented views are regulated with consistent class predictions, and 2) ensuring that different data of one class are regulated with similar feature embeddings. Extensive experiments demonstrate the effectiveness of DualMatch. In the standard SSL setting, the proposal achieves 9% error reduction compared with SOTA methods, even in a more challenging class-imbalanced setting, the proposal can still achieve 6% error reduction. Code is available at https://github.com/CWangAI/DualMatch

翻译：半监督学习为标签不足时利用未标记数据提供了一种富有表现力的框架。以往的半监督学习方法通常以单层级交互方式匹配不同数据增强视图的模型预测，这高度依赖于伪标签的质量，导致半监督学习缺乏鲁棒性。本文提出一种名为DualMatch的新型半监督学习方法，该方法通过双层级交互方式联合调用类别预测与特征嵌入。DualMatch要求数据增强具有一致性正则化，具体而言：1）确保不同增强视图受一致类别预测的约束；2）确保同一类别的不同数据受相似特征嵌入的约束。大量实验证明了DualMatch的有效性。在标准半监督学习设置下，该方法相比最先进方法实现了9%的错误率降低；即使在更具挑战性的类别不平衡设置下，该方法仍能实现6%的错误率降低。代码已开源：https://github.com/CWangAI/DualMatch