  Unsupervised domain adaptation (UDA) becomes more and more popular in
tackling real-world problems without ground truth of the target domain. Though
a mass of tedious annotation work is not needed, UDA unavoidably faces the
problem how to narrow the domain discrepancy to boost the transferring
performance. In this paper, we focus on UDA for semantic segmentation task.
Firstly, we propose a style-independent content feature extraction mechanism to
keep the style information of extracted features in the similar space, since
the style information plays a extremely slight role for semantic segmentation
compared with the content part. Secondly, to keep the balance of pseudo labels
on each category, we propose a category-guided threshold mechanism to choose
category-wise pseudo labels for self-supervised learning. The experiments are
conducted using GTA5 as the source domain, Cityscapes as the target domain. The
results show that our model outperforms the state-of-the-arts with a noticeable
gain on cross-domain adaptation tasks.
