  Image-to-image translation is a long-established and a difficult problem in
computer vision. In this paper we propose an adversarial based model for
image-to-image translation. The regular deep neural-network based methods
perform the task of image-to-image translation by comparing gram matrices and
using image segmentation which requires human intervention. Our generative
adversarial network based model works on a conditional probability approach.
This approach makes the image translation independent of any local, global and
content or style features. In our approach we use a bidirectional
reconstruction model appended with the affine transform factor that helps in
conserving the content and photorealism as compared to other models. The
advantage of using such an approach is that the image-to-image translation is
semi-supervised, independant of image segmentation and inherits the properties
of generative adversarial networks tending to produce realistic. This method
has proven to produce better results than Multimodal Unsupervised
Image-to-image translation.
