  Visual Domain Adaptation is a problem of immense importance in computer
vision. Previous approaches showcase the inability of even deep neural networks
to learn informative representations across domain shift. This problem is more
severe for tasks where acquiring hand labeled data is extremely hard and
tedious. In this work, we focus on adapting the representations learned by
segmentation networks across synthetic and real domains. Contrary to previous
approaches that use a simple adversarial objective or superpixel information to
aid the process, we propose an approach based on Generative Adversarial
Networks (GANs) that brings the embeddings closer in the learned feature space.
To showcase the generality and scalability of our approach, we show that we can
achieve state of the art results on two challenging scenarios of synthetic to
real domain adaptation. Additional exploratory experiments show that our
approach: (1) generalizes to unseen domains and (2) results in improved
alignment of source and target distributions.
