Semi-supervised learning provides a solution to reduce the dependency of
machine learning on labeled data. As one of the efficient semi-supervised
techniques, self-training (ST) has received increasing attention. Several
advancements have emerged to address challenges associated with noisy
pseudo-labels. Previous works on self-training acknowledge the importance of
unlabeled data but have not delved into their efficient utilization, nor have
they paid attention to the problem of high time consumption caused by iterative
learning. This paper proposes Incremental Self-training (IST) for
semi-supervised learning to fill these gaps. Unlike ST, which processes all
data indiscriminately, IST processes data in batches and priority assigns
pseudo-labels to unlabeled samples with high certainty. Then, it processes the
data around the decision boundary after the model is stabilized, enhancing
classifier performance. Our IST is simple yet effective and fits existing
self-training-based semi-supervised learning methods. We verify the proposed
IST on five datasets and two types of backbone, effectively improving the
recognition accuracy and learning speed. Significantly, it outperforms
state-of-the-art competitors on three challenging image classification tasks.

本研究提出了增量自训练（IST）用于半监督学习，通过在模型稳定后处理决策边界附近的数据，优化分类器性能，并在五个数据集和两种基础模型上验证，取得了显著的识别准确率和学习速度的提升，超越了现有最先进方法三个具有挑战性的图像分类任务。