mlsanityData to 14 Oct 2026

Unsupervised Image-to-Image Translation Networks

arXiv 1703.00848 ↗First posted 2 Mar 2017cs.CV cs.AIcited by 603

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Cited by 49 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in https://github.com/mingyuliutw/unit .

Key facts. Cited by 603 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Mar 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 192; 2022: 128; 2023: 116; 2024: 87; 2025: 45; 2026: 34.

603 papers.

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