  Performance achievable by modern deep learning approaches are directly
related to the amount of data used at training time. Unfortunately, the
annotation process is notoriously tedious and expensive, especially for
pixel-wise tasks like semantic segmentation. Recent works have proposed to rely
on synthetically generated imagery to ease the training set creation. However,
models trained on these kind of data usually under-perform on real images due
to the well known issue of domain shift. We address this problem by learning a
domain-to-domain image translation GAN to shrink the gap between real and
synthetic images. Peculiarly to our method, we introduce semantic constraints
into the generation process to both avoid artifacts and guide the synthesis. To
prove the effectiveness of our proposal, we show how a semantic segmentation
CNN trained on images from the synthetic GTA dataset adapted by our method can
improve performance by more than 16% mIoU with respect to the same model
trained on synthetic images.
