  The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised
domain adaptive model in semantic segmentation tasks for industrial waste
sorting. In this paper, we introduce the SIA_Adapt method, which incorporates
several methods for domain adaptive models. The core of our method in the
transferable representation from large-scale pre-training. In this process, we
choose a network architecture that differs from the state-of-the-art for domain
adaptation. After that, self-training using pseudo-labels helps to make the
initial adaptation model more adaptable to the target domain. Finally, the
model soup scheme helped to improve the generalization performance in the
target domain. Our method SIA_Adapt achieves 1st place in the VisDA2022
challenge. The code is available on https:
//github.com/DaehanKim-Korea/VisDA2022_Winner_Solution.
