Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL methods face is the confirmation bias, which occurs when the model is overfitted to the small labeled training dataset and produces overconfident, incorrect predictions. To address this issue, we propose SequenceMatch, an efficient SSL method that utilizes multiple data augmentations. The key element of SequenceMatch is the inclusion of a medium augmentation for unlabeled data. By taking advantage of different augmentations and the consistency constraints between each pair of augmented examples, SequenceMatch helps reduce the divergence between the prediction distribution of the model for weakly and strongly augmented examples. In addition, SequenceMatch defines two different consistency constraints for high and low-confidence predictions. As a result, SequenceMatch is more data-efficient than ReMixMatch, and more time-efficient than both ReMixMatch ($\times4$) and CoMatch ($\times2$) while having higher accuracy. Despite its simplicity, SequenceMatch consistently outperforms prior methods on standard benchmarks, such as CIFAR-10/100, SVHN, and STL-10. It also surpasses prior state-of-the-art methods by a large margin on large-scale datasets such as ImageNet, with a 38.46\% error rate. Code is available at https://github.com/beandkay/SequenceMatch.

翻译：近年来，半监督学习（SSL）因能够利用大量无标签数据训练模型而广受欢迎。然而，许多SSL方法面临的一个问题是确认偏差——模型因过度拟合少量有标签训练数据而产生过度自信的错误预测。为了解决这一问题，我们提出SequenceMatch，一种利用多重数据增强的高效SSL方法。SequenceMatch的核心在于为无标签数据引入中等强度增强。通过充分利用不同增强策略以及每对增强样本之间的一致性约束，SequenceMatch有助于减小模型对弱增强和强增强样本预测分布之间的差异。此外，SequenceMatch针对高置信度与低置信度预测定义了两种不同的一致性约束。实验表明，SequenceMatch在数据效率上优于ReMixMatch，在时间效率上分别比ReMixMatch（×4）和CoMatch（×2）提升4倍和2倍，同时保持更高精度。尽管方法简洁，SequenceMatch在CIFAR-10/100、SVHN和STL-10等标准基准上持续优于先前方法，并在ImageNet等大规模数据集上以38.46%的错误率大幅超越现有最优方法。代码开源地址：https://github.com/beandkay/SequenceMatch。