  Current unsupervised domain adaptation methods can address many types of
distribution shift, but they assume data from the source domain is freely
available. As the use of pre-trained models becomes more prevalent, it is
reasonable to assume that source data is unavailable. We propose an
unsupervised method for adapting a source classifier to a target domain that
varies from the source domain along natural axes, such as brightness and
contrast. Our method only requires access to unlabeled target instances and the
source classifier. We validate our method in scenarios where the distribution
shift involves brightness, contrast, and rotation and show that it outperforms
fine-tuning baselines in scenarios with limited labeled data.
