  Domain adaptation is of huge interest as labeling is an expensive and
error-prone task, especially when labels are needed on pixel-level like in
semantic segmentation. Therefore, one would like to be able to train neural
networks on synthetic domains, where data is abundant and labels are precise.
However, these models often perform poorly on out-of-domain images. To mitigate
the shift in the input, image-to-image approaches can be used. Nevertheless,
standard image-to-image approaches that bridge the domain of deployment with
the synthetic training domain do not focus on the downstream task but only on
the visual inspection level. We therefore propose a "task aware" version of a
GAN in an image-to-image domain adaptation approach. With the help of a small
amount of labeled ground truth data, we guide the image-to-image translation to
a more suitable input image for a semantic segmentation network trained on
synthetic data (synthetic-domain expert). The main contributions of this work
are 1) a modular semi-supervised domain adaptation method for semantic
segmentation by training a downstream task aware CycleGAN while refraining from
adapting the synthetic semantic segmentation expert 2) the demonstration that
the method is applicable to complex domain adaptation tasks and 3) a less
biased domain gap analysis by using from scratch networks. We evaluate our
method on a classification task as well as on semantic segmentation. Our
experiments demonstrate that our method outperforms CycleGAN - a standard
image-to-image approach - by 7 percent points in accuracy in a classification
task using only 70 (10%) ground truth images. For semantic segmentation we can
show an improvement of about 4 to 7 percent points in mean Intersection over
union on the Cityscapes evaluation dataset with only 14 ground truth images
during training.
