Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
Implementation:-
$ cd cyclegan/ $ bash download_dataset.sh apple2orange $ python3 cyclegan.py
The implementation of the Cycle GAN is for implementing the transformation of Apple to Orange and Orange to Apple.
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y → X and introduce a cycle consistency loss to enforce F(G(X)) ≈ X (and vice versa).
Paper: https://arxiv.org/abs/1703.10593
