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
Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images
qiangwang57/coreg_flim_histology
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Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples.
Separating Content and Style for Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples.
Estimating Image Depth in the Comics Domain
IVRL/ComicsDepth
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Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy.
Unaligned Image-to-Image Translation by Learning to Reweight
An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e. g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain.
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
Cardio-AI/detcyclegan_pytorch
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In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance.
Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation
Elvinky/IEGAN
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To address this issue, we propose a novel architecture, termed as IEGAN, which removes the encoder of each network and introduces an encoder that is independent of other networks.
Underwater Image Restoration via Contrastive Learning and a Real-world Dataset
JunlinHan/CWR
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There are 2000 reference restored images and 6003 original underwater images in the unpaired training set.
Dual Contrastive Learning for Unsupervised Image-to-Image Translation
JunlinHan/DCLGAN
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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.
SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation
NetEase-GameAI/SPatchGAN
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ICCV 2021
For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches.
Single Underwater Image Restoration by Contrastive Learning
JunlinHan/CWR
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Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world.
