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 )
Libraries
Use these libraries to find Unsupervised Image-To-Image Translation models and implementationsMost implemented papers
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Domain adaptation is critical for success in new, unseen environments.
Dual Contrastive Learning for Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data.
Unsupervised Attention-guided Image to Image Translation
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.
One-Shot Unsupervised Cross Domain Translation
Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B.
Demystifying Inter-Class Disentanglement
Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence.
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied.
Lifespan Age Transformation Synthesis
Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process.
The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation is an inherently ill-posed problem.
Learning to generate line drawings that convey geometry and semantics
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.