Confidence Regularized Self-Training

Understand

Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.

• These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining.
• However, since pseudo-labels can be noisy, self-training can put overconfident label belief on wrong classes, leading to deviated solutions with propagated errors.
• To address the problem, we propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training.