Multimodal Unsupervised Image-To-Image Translation
14 papers with code • 6 benchmarks • 4 datasets
Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.
( Image credit: MUNIT: Multimodal UNsupervised Image-to-image Translation )
Benchmarks
These leaderboards are used to track progress in Multimodal Unsupervised Image-To-Image Translation
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Libraries
Use these libraries to find Multimodal Unsupervised Image-To-Image Translation models and implementations
CelebA-HQ
AFHQ
CATS
FFHQ-Aging
Latest papers
Multimodal Unsupervised Image-to-Image Translation
eriklindernoren/PyTorch-GAN
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ECCV 2018
To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain.
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
PramuPerera/In2I
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In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
tensorflow/models
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ICCV 2017
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.
Unsupervised Image-to-Image Translation Networks
eriklindernoren/PyTorch-GAN
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NeurIPS 2017
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.
