Abstract: Unsupervised domain adaptation aims to learn a prediction model that generalizes well on a target domain given labeled source data and unlabeled target data. However, source data sometimes can be unavailable due to data privacy or decentralized learning architectures. In this paper, we address the source-free unsupervised domain adaptation problem where only the pretrained source model and unlabeled target data are given. To this end, we propose an Augmented Self-Labeling (ASL) method that jointly optimizes the prediction model and the pseudo-labels for the target data starting from the initial source model. It involves two alternating steps: augmented self-labeling improves pseudo-labels by solving an optimal transport problem with the Sinkhorn-Knopp algorithm, and model re-training trains the model with the supervision of improved pseudo-labels. We further introduce model regularization terms to improve the model re-training. Experiments show that our method achieves comparable or better results than the state-of-the-art methods on the standard benchmarks.