Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks: https://junyanz.github.io/CycleGAN/
- CycleGAN TensorFlow tutorial: CycleGAN TensorFlow
- CycleGAN Paper: https://arxiv.org/abs/1703.10593 (“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”)
- Github: https://github.com/junyanz/CycleGAN (“CycleGAN”)
- CycleGAN Discussion: https://www.reddit.com/r/MachineLearning/comments/62ufdx/r_unpaired_imagetoimage_translation_using/
Abstract: 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.
Cycle-Consistent Adversarial Networks Datasets:
facades: 400 images from the CMP Facades dataset.
cityscapes: 2975 images from the Cityscapes training set.
maps: 1096 training images scraped from Google Maps.
horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords
apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet using keywords
summer2winter_yosemite: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using combination of tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
iphone2dslr_flower: both classe of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.