Unsupervised Image-to-Image Translation Networks

NeurIPS 2017 Ming-Yu LiuThomas BreuelJan Kautz

Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Multimodal Unsupervised Image-To-Image Translation Cats-and-Dogs UNIT CIS 0.115 # 2
IS 0.826 # 2
Multimodal Unsupervised Image-To-Image Translation Edge-to-Handbags UNIT Quality 37.3% # 4
Diversity 0.023 # 3
Multimodal Unsupervised Image-To-Image Translation Edge-to-Shoes UNIT Quality 37.4% # 3
Diversity 0.011 # 3
Multimodal Unsupervised Image-To-Image Translation EPFL NIR-VIS UNIT PSNR 15.33 # 3
Unsupervised Image-To-Image Translation Freiburg Forest Dataset UNIT PSNR 9.42 # 3

Methods used in the Paper


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