Self-Ensemble Variance Regularization for Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a label-rich source domain to a different yet related fully-unlabeled target domain. Existing approaches utilize self-training scheme to learn discriminative target features and thus enforce class-level distribution alignment implicitly across the source and target domains. However, inherent noise of the pseudo labels due to domain shift could compromise the training process to negatively affect the adapted model performance. In this paper, we propose Self-Ensemble Variance Regularization for Domain Adaptaton (VRDA) method to rectify the learning with pseudo labels. To be specific, we regard the prediction distinction between the student and its self-ensemble teacher model as prediction variance, to regularize target domain prediction bias from pseudo labels. The experimental results reveal that the proposed VRDA achieves the state-of-the-art performance on several standard UDA datasets.