  In terms of Image-to-image translation, Generative Adversarial Networks
(GANs) has achieved great success even when it is used in the unsupervised
dataset. In this work, we aim to translate cartoon images to photo-realistic
images using GAN. We apply several state-of-the-art models to perform this
task; however, they fail to perform good quality translations. We observe that
the shallow difference between these two domains causes this issue. Based on
this idea, we propose a method based on CycleGAN model for image translation
from cartoon domain to photo-realistic domain. To make our model efficient, we
implemented Spectral Normalization which added stability in our model. We
demonstrate our experimental results and show that our proposed model has
achieved the lowest Frechet Inception Distance score and better results
compared to another state-of-the-art technique, UNIT.
