  The original publication Unpaired Image-to-Image Translation using
Cycle-Consistent Adversarial Networks served as the inspiration for this
implementation project. Researchers developed a novel method for doing
image-to-image translations using an unpaired dataset in the original study.
Despite the fact that the pix2pix models findings are good, the matched dataset
is frequently not available. In the absence of paired data, cycleGAN can
therefore get over this issue by converting images to images. In order to
lessen the difference between the images, they implemented cycle consistency
loss.I evaluated CycleGAN with three different datasets, and this paper briefly
discusses the findings and conclusions.
