Semi-supervised learning approaches train on small sets of labeled data along
with large sets of unlabeled data. Self-training is a semi-supervised
teacher-student approach that often suffers from the problem of "confirmation
bias" that occurs when the student model repeatedly overfits to incorrect
pseudo-labels given by the teacher model for the unlabeled data. This bias
impedes improvements in pseudo-label accuracy across self-training iterations,
leading to unwanted saturation in model performance after just a few
iterations. In this work, we describe multiple enhancements to improve the
self-training pipeline to mitigate the effect of confirmation bias. We evaluate
our enhancements over multiple datasets showing performance gains over existing
self-training design choices. Finally, we also study the extendability of our
enhanced approach to Open Set unlabeled data (containing classes not seen in
labeled data).

本文介绍了多种改进的方法以减轻自我训练管道中的确认偏差，评估了这些改进在多个数据集上的表现，证明在现有的自我训练设计选择上实现了性能增益，并研究了增强方法对未知类别的 Open Set 无标签数据的扩展性。