  Unpaired Image-to-Image translation aims to convert the image from one domain
(input domain A) to another domain (target domain B), without providing paired
examples for the training. The state-of-the-art, Cycle-GAN demonstrated the
power of Generative Adversarial Networks with Cycle-Consistency Loss. While its
results are promising, there is scope for optimization in the training process.
This paper introduces a new neural network architecture, which only learns the
translation from domain A to B and eliminates the need for reverse mapping (B
to A), by introducing a new Deviation-loss term. Furthermore, few other
improvements to the Cycle-GAN are found and utilized in this new architecture,
contributing to significantly lesser training duration.
