  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. Our method treats
pseudo-labels as continuous latent variables jointly optimized via alternating
optimization. We propose two types of confidence regularization: label
regularization (LR) and model regularization (MR). CRST-LR generates soft
pseudo-labels while CRST-MR encourages the smoothness on network output.
Extensive experiments on image classification and semantic segmentation show
that CRSTs outperform their non-regularized counterpart with state-of-the-art
performance. The code and models of this work are available at
https://github.com/yzou2/CRST.
