  Domain adaptation for semantic segmentation enables to alleviate the need for
large-scale pixel-wise annotations. Recently, self-supervised learning (SSL)
with a combination of image-to-image translation shows great effectiveness in
adaptive segmentation. The most common practice is to perform SSL along with
image translation to well align a single domain (the source or target).
However, in this single-domain paradigm, unavoidable visual inconsistency
raised by image translation may affect subsequent learning. In this paper,
based on the observation that domain adaptation frameworks performed in the
source and target domain are almost complementary in terms of image translation
and SSL, we propose a novel dual path learning (DPL) framework to alleviate
visual inconsistency. Concretely, DPL contains two complementary and
interactive single-domain adaptation pipelines aligned in source and target
domain respectively. The inference of DPL is extremely simple, only one
segmentation model in the target domain is employed. Novel technologies such as
dual path image translation and dual path adaptive segmentation are proposed to
make two paths promote each other in an interactive manner. Experiments on
GTA5$\rightarrow$Cityscapes and SYNTHIA$\rightarrow$Cityscapes scenarios
demonstrate the superiority of our DPL model over the state-of-the-art methods.
The code and models are available at: \url{https://github.com/royee182/DPL}
