  Most unsupervised domain adaptation (UDA) methods assume that labeled source
images are available during model adaptation. However, this assumption is often
infeasible owing to confidentiality issues or memory constraints on mobile
devices. Some recently developed approaches do not require source images during
adaptation, but they show limited performance on perturbed images. To address
these problems, we propose a novel source-free UDA method that uses only a
pre-trained source model and unlabeled target images. Our method captures the
aleatoric uncertainty by incorporating data augmentation and trains the feature
generator with two consistency objectives. The feature generator is encouraged
to learn consistent visual features away from the decision boundaries of the
head classifier. Thus, the adapted model becomes more robust to image
perturbations. Inspired by self-supervised learning, our method promotes
inter-space alignment between the prediction space and the feature space while
incorporating intra-space consistency within the feature space to reduce the
domain gap between the source and target domains. We also consider epistemic
uncertainty to boost the model adaptation performance. Extensive experiments on
popular UDA benchmark datasets demonstrate that the proposed source-free method
is comparable or even superior to vanilla UDA methods. Moreover, the adapted
models show more robust results when input images are perturbed.
