horse zebra zebra horse Summer Winter summer winter winter summer Photograph Van Gogh Cezanne Monet Ukiyo-e Monet Photos Monet photo photo Monet Figure 1: Given any two unordered image collections X and Y , our algorithm learns to automatically "translate" an image from one into the other and vice versa. Example application (bottom): using a collection of paintings of a famous artist, learn to render a user's photograph into their style. 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 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. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Most of the architectures are based on learning to translate images from domain A to domain B via mapping G while they learn to convert images from B to A in mapping F. The cycle-consistency loss REF can prevent the two learned mappings from contradicting each other.
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science