We added pseudo-labels to the unlabeled data.
我们给未标注数据加入了伪标签。
After the model reached a stable accuracy, it generated pseudo-labels for new samples, which helped improve performance without extra annotation.
当模型准确率稳定后,它为新样本生成伪标签,从而在不增加额外人工标注的情况下提升了性能。
Dong-Hyun Lee (2013): Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks(提出并推广“pseudo-label / pseudo-labeling”在深度学习半监督训练中的用法)
Kihyuk Sohn et al. (2020): FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence(方法中使用高置信度预测作为伪标签)
Olivier Chapelle, Bernhard Schölkopf, Alexander Zien (eds.) (2006): Semi-Supervised Learning(半监督学习经典著作,讨论与伪标签/自训练相近的思路与方法)