  Deep Learning for Computer Vision depends mainly on the source of
supervision.Photo-realistic simulators can generate large-scale automatically
labeled syntheticdata, but introduce a domain gap negatively impacting
performance. We propose anew unsupervised domain adaptation algorithm, called
SPIGAN, relying on Sim-ulator Privileged Information (PI) and Generative
Adversarial Networks (GAN).We use internal data from the simulator as PI during
the training of a target tasknetwork. We experimentally evaluate our approach
on semantic segmentation. Wetrain the networks on real-world Cityscapes and
Vistas datasets, using only unla-beled real-world images and synthetic labeled
data with z-buffer (depth) PI fromthe SYNTHIA dataset. Our method improves over
no adaptation and state-of-the-art unsupervised domain adaptation techniques.
