Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks

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 push F(G(X)) ~ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.