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
this https URL

本文介绍了一种新的无监督域适应方法，通过图像翻译表达源模型与目标模型之间的知识差异， 使用该方法为目标模型生成源风格的图像，实现进一步调整目标模型，从而避免了访问源数据的需要。