Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

ICCV 2017 Jun-Yan ZhuTaesung ParkPhillip IsolaAlexei A. Efros

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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Multimodal Unsupervised Image-To-Image Translation Cats-and-Dogs CycleGAN CIS 0.076 # 3
IS 0.813 # 3
Image-to-Image Translation Cityscapes Labels-to-Photo CycleGAN Class IOU 0.11 # 2
Per-class Accuracy 17% # 2
Per-pixel Accuracy 52% # 7
Image-to-Image Translation Cityscapes Photo-to-Labels CycleGAN Per-pixel Accuracy 58% # 2
Per-class Accuracy 22% # 2
Class IOU 0.16 # 2
Multimodal Unsupervised Image-To-Image Translation Edge-to-Handbags CycleGAN Quality 40.8% # 3
Diversity 0.012 # 4
Multimodal Unsupervised Image-To-Image Translation Edge-to-Shoes CycleGAN Quality 36.0% # 4
Diversity 0.010 # 4
Multimodal Unsupervised Image-To-Image Translation EPFL NIR-VIS cycGAN PSNR 17.38 # 2
Unsupervised Image-To-Image Translation Freiburg Forest Dataset cycGAN PSNR 18.57 # 2
Image-to-Image Translation RaFD CycleGAN Classification Error 5.99% # 3

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Facial Expression Translation CelebA CycleGAN AMT 34.6 # 2

Methods used in the Paper