  Domain shift is a very challenging problem for semantic segmentation. Any
model can be easily trained on synthetic data, where images and labels are
artificially generated, but it will perform poorly when deployed on real
environments. In this paper, we address the problem of domain adaptation for
semantic segmentation of street scenes. Many state-of-the-art approaches focus
on translating the source image while imposing that the result should be
semantically consistent with the input. However, we advocate that the image
semantics can also be exploited to guide the translation algorithm. To this
end, we rethink the generative model to enforce this assumption and strengthen
the connection between pixel-level and feature-level domain alignment. We
conduct extensive experiments by training common semantic segmentation models
with our method and show that the results we obtain on the synthetic-to-real
benchmarks surpass the state-of-the-art.
