  Image-to-image translation models transfer images from input domain to output
domain in an endeavor to retain the original content of the image. Contrastive
Unpaired Translation is one of the existing methods for solving such problems.
Significant advantage of this method, compared to competitors, is the ability
to train and perform well in cases where both input and output domains are only
a single image. Another key thing that differentiates this method from its
predecessors is the usage of image patches rather than the whole images. It
also turns out that sampling negatives (patches required to calculate the loss)
from the same image achieves better results than a scenario where the negatives
are sampled from other images in the dataset. This type of approach encourages
mapping of corresponding patches to the same location in relation to other
patches (negatives) while at the same time improves the output image quality
and significantly decreases memory usage as well as the time required to train
the model compared to CycleGAN method used as a baseline. Through a series of
experiments we show that using focal loss in place of cross-entropy loss within
the PatchNCE loss can improve on the model's performance and even surpass the
current state-of-the-art model for image-to-image translation.
