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.

本文介绍了基于样本加权统一公式的伪标签方法及其 inherent quantity-quality trade-off 问题, 并提出了利用截断高斯函数对置信度加权以实现软的置信度阈值。加强了弱学习类别的利用, 在图像分类、文本分类和非平衡分类等多种基准测试中都取得了显著的改进。

SoftMatch:半监督学习中解决数量-质量平衡问题