Design Choices for Enhancing Noisy Student Self-Training

Semi-supervised learning approaches train on small sets of labeled data in addition to large sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that often suffers from "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 study multiple design choices to improve the Noisy Student self-training pipeline and reduce confirmation bias. We showed that our proposed Weighted SplitBatch Sampler and Dataset-Adaptive Techniques for Model Calibration and Entropy-Based Pseudo-Label Selection provided performance gains over existing design choices across multiple datasets. Finally, we also study the extendability of our enhanced approach to Open Set unlabeled data (containing classes not seen in labeled data). The source code can be licensed for use via email.