We introduce MarginMatch, a new SSL approach combining consistency regularization and pseudo-labeling, with its main novelty arising from the use of unlabeled data training dynamics to measure pseudo-label quality. Instead of using only the model's confidence on an unlabeled example at an arbitrary iteration to decide if the example should be masked or not, MarginMatch also analyzes the behavior of the model on the pseudo-labeled examples as the training progresses, to ensure low quality predictions are masked out. MarginMatch brings substantial improvements on four vision benchmarks in low data regimes and on two large-scale datasets, emphasizing the importance of enforcing high-quality pseudo-labels. Notably, we obtain an improvement in error rate over the state-of-the-art of 3.25% on CIFAR-100 with only 25 labels per class and of 3.78% on STL-10 using as few as 4 labels per class. We make our code available at https://github.com/tsosea2/MarginMatch.

MarginMatch是一种结合一致性正则化和伪标记的新的SSL方法，其主要创新在于使用无标签数据的训练动态来衡量伪标签的质量，以确保屏蔽低质量预测；在低数据情况下，MarginMatch在四个视觉基准测试以及两个大规模数据集上都取得了显著的改进，并且在每类只有25个标签的CIFAR-100上提高了3.25％的错误率，在每类只有4个标签的STL-10上提高了3.78％的错误率。

MarginMatch: 利用伪边际改进半监督学习