Content provided by Youshan Zhang, the first author of the paper Impact of ImageNet Model Selection on Domain Adaptation. It is known that training and updating of the machine learning model depend on data annotation. We often have a serious problem that lacks labeled data for training in the real world. Therefore, it is often necessary to transfer knowledge from an existing labeled domain to an unlabeled new domain. However, due to the phenomenon of data bias or domain shift, machine learning models do not generalize well from an existing domain to a novel unlabeled domain. Domain adaptation has been a promising method to mitigate the domain shift problem.