  While developing perception based deep learning models, the benefit of
synthetic data is enormous. However, performance of networks trained with
synthetic data for certain computer vision tasks degrade significantly when
tested on real world data due to the domain gap between them. One of the
popular solutions in bridging this gap between synthetic and actual world data
is to frame it as a domain adaptation task. In this paper, we propose and
evaluate novel ways for the betterment of such approaches. In particular we
build upon the method of UNIT-GAN.
  In normal GAN training for the task of domain translation, pairing of images
from both the domains (viz, real and synthetic) is done randomly. We propose a
novel method to efficiently incorporate semantic supervision into this pair
selection, which helps in boosting the performance of the model along with
improving the visual quality of such transformed images. We illustrate our
empirical findings on Cityscapes \cite{cityscapes} and challenging synthetic
dataset Synscapes. Though the findings are reported on the base network of
UNIT-GAN, they can be easily extended to any other similar network.
