  Collecting well-annotated image datasets to train modern machine learning
algorithms is prohibitively expensive for many tasks. One appealing alternative
is rendering synthetic data where ground-truth annotations are generated
automatically. Unfortunately, models trained purely on rendered images often
fail to generalize to real images. To address this shortcoming, prior work
introduced unsupervised domain adaptation algorithms that attempt to map
representations between the two domains or learn to extract features that are
domain-invariant. In this work, we present a new approach that learns, in an
unsupervised manner, a transformation in the pixel space from one domain to the
other. Our generative adversarial network (GAN)-based method adapts
source-domain images to appear as if drawn from the target domain. Our approach
not only produces plausible samples, but also outperforms the state-of-the-art
on a number of unsupervised domain adaptation scenarios by large margins.
Finally, we demonstrate that the adaptation process generalizes to object
classes unseen during training.
