Hand-on Implementation of CycleGAN, Image-to-Image Translation using PyTorch

A CycleGAN is designed for image-to-image translation, and it learns from unpaired training data. It gives us a way to learn the mapping between one image domain and another using an unsupervised approach. Jun-Yan Zhu original paper on the CycleGan can be found here who is Assistant Professor in the School of Computer Science of Carnegie Mellon University. These images do not come with the labels, i.e. the generator creates the training data X from the Y datasets. We do not have to extract all the corresponding features from the individual images.