Unsupervised Image-To-Image Translation
69 papers with code • 2 benchmarks • 2 datasets
Unsupervised image-to-image translation is the task of doing image-to-image translation without ground truth image-to-image pairings.
( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )
Benchmarks
These leaderboards are used to track progress in Unsupervised Image-To-Image Translation
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Libraries
Use these libraries to find Unsupervised Image-To-Image Translation models and implementations
MNIST
Freiburg Forest
Latest papers
Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator
rajiv1990/Unsupervised-Cross-domain-I2I-using-a-Shared-Discriminator
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We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain.
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network
HideUnderBush/UI2I_via_StyleGAN2
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We proposed a new I2I translation method that generates a new model in the target domain via a series of model transformations on a pre-trained StyleGAN2 model in the source domain.
Spectral Synthesis for Satellite-to-Satellite Translation
anonymous-ai-for-earth/satellite-to-satellite-translation
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These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
yhlleo/DWC-GAN
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Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
Toward Zero-Shot Unsupervised Image-to-Image Translation
cyq373/ZUNIT
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Recent studies have shown remarkable success in unsupervised image-to-image translation.
The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation is an inherently ill-posed problem.
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
nvlabs/imaginaire
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ECCV 2020
Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping.
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
yuzhenmao/MI_P2V
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We present a mutual information-based framework for unsupervised image-to-image translation.
Rethinking the Truly Unsupervised Image-to-Image Translation
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains.
