Title: Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation. Abstract: 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.  Additionally, most UDA methods directly adapt from source to target domain without regard for the large domain discrepancy.  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 network to train with. 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 along with class subset sampling and clustering based pseudo-label refinement achieves state-of-the-art performance on two public UDA benchmark datasets: Office-31, and Office-Home.