PROGRESSIVE MIXUP AUGMENTED TEACHER-STUDENT LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, mostly through learning a domain invariant feature representation. Currently, the best performing UDA methods use category level domain alignment to capture fine-grained information, resulting in significantly improved performance over global alignment. While successful, category level UDA methods suffer from the unreliable pseudo-labels for target data. In this paper, we propose an UDA approach with teacher-student learning where the teacher network is used to provide more reliable target pseudo-labels for the student during training. Furthermore, we use a progressive mixup augmentation strategy which generates intermediate samples that become increasingly target-dominant as training progresses. Aligning the source and intermediate domains allows the model to gradually transfer fine-grained domain knowledge from the source to the target domain while minimizing the negative impact of noisy target pseudo-labels. This progressive mixup augmented teacher-student (PMATS) training strategy achieves state-of-the-art performance on two public UDA benchmark datasets: Office-31 and Office-Home.