  Unsupervised domain adaptation (UDA) becomes more and more popular in
tackling real-world problems without ground truth of the target domain. Though
tedious annotation work is not required, UDA unavoidably faces two problems: 1)
how to narrow the domain discrepancy to boost the transferring performance; 2)
how to improve pseudo annotation producing mechanism for self-supervised
learning (SSL). In this paper, we focus on UDA for semantic segmentation task.
Firstly, we introduce adversarial learning into style gap bridging mechanism to
keep the style information from two domains in the similar space. Secondly, to
keep the balance of pseudo labels on each category, we propose a
category-adaptive threshold mechanism to choose category-wise pseudo labels for
SSL. 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.
