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
Most implemented papers
Learning to generate line drawings that convey geometry and semantics
carolineec/informative-drawings
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CVPR 2022
We introduce a geometry loss which predicts depth information from the image features of a line drawing, and a semantic loss which matches the CLIP features of a line drawing with its corresponding photograph.
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification
Hindawi91/Pseudo_Supervised_Metrics
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Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain.
One-Sided Unsupervised Domain Mapping
sagiebenaim/DistanceGAN
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NeurIPS 2017
In this work, we present a method of learning $G_{AB}$ without learning $G_{BA}$.
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.
Estimating the Success of Unsupervised Image to Image Translation
sagiebenaim/gan_bound
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ECCV 2018
While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way.
Unsupervised Video-to-Video Translation
dbash/CycleGAN3D
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ICLR 2019
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.
Refacing: reconstructing anonymized facial features using GANs
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing.
Unsupervised Attention-guided Image-to-Image Translation
AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation
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NeurIPS 2018
Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.
InstaGAN: Instance-aware Image-to-Image Translation
Our comparative evaluation demonstrates the effectiveness of the proposed method on different image datasets, in particular, in the aforementioned challenging cases.
Unsupervised Image-to-Image Translation with Self-Attention Networks
itsss/img2img_sa
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Unsupervised image translation aims to learn the transformation from a source domain to another target domain given unpaired training data.
