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Since there exists an infinite set of joint\ndistributions that can arrive the given marginal distributions, one could infer\nnothing about the joint distribution from the marginal distributions without\nadditional assumptions. To address the problem, we make a shared-latent space\nassumption and propose an unsupervised image-to-image translation framework\nbased on Coupled GANs. We compare the proposed framework with competing\napproaches and present high quality image translation results on various\nchallenging unsupervised image translation tasks, including street scene image\ntranslation, animal image translation, and face image translation. We also\napply the proposed framework to domain adaptation and achieve state-of-the-art\nperformance on benchmark datasets. 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