The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate the inherent quantity-quality trade-off problem of pseudo-labeling with thresholding, which may prohibit learning. To this end, we propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training, effectively exploiting the unlabeled data. We derive a truncated Gaussian function to weight samples based on their confidence, which can be viewed as a soft version of the confidence threshold. We further enhance the utilization of weakly-learned classes by proposing a uniform alignment approach. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.

翻译：半监督学习（SSL）的关键挑战在于如何有效利用有限的标注数据和海量未标注数据，以提升模型的泛化性能。本文首先通过统一的样本加权公式重新审视了流行的伪标签方法，并揭示了基于阈值的伪标签方法中固有的数量-质量权衡问题，该问题可能阻碍学习。为此，我们提出SoftMatch方法，通过在训练过程中同时保持伪标签的高数量和高质量，有效利用未标注数据来克服这一权衡。我们推导出一个截断高斯函数，根据样本的置信度进行加权，可视为置信度阈值的软版本。进一步地，我们提出一种均匀对齐方法，以增强对弱学习类别的利用。实验表明，SoftMatch在包括图像、文本和不平衡分类在内的多种基准任务上均取得了显著改进。