  A source model trained on source data and a target model learned through
unsupervised domain adaptation (UDA) usually encode different knowledge. To
understand the adaptation process, we portray their knowledge difference with
image translation. Specifically, we feed a translated image and its original
version to the two models respectively, formulating two branches. Through
updating the translated image, we force similar outputs from the two branches.
When such requirements are met, differences between the two images can
compensate for and hence represent the knowledge difference between models. To
enforce similar outputs from the two branches and depict the adapted knowledge,
we propose a source-free image translation method that generates source-style
images using only target images and the two models. We visualize the adapted
knowledge on several datasets with different UDA methods and find that
generated images successfully capture the style difference between the two
domains. For application, we show that generated images enable further tuning
of the target model without accessing source data. Code available at
https://github.com/hou-yz/DA_visualization.
