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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find Unsupervised Image-To-Image Translation models and implementations
MNIST
Freiburg Forest
Latest papers with no code
Dense Multitask Learning to Reconfigure Comics
In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narratives.
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions.
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation
Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks.
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology
Baseline metrics are set by training and testing the baseline classification model on a reference stain.
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
The network is especially for those image datasets suffering from the following two significant limitations: 1) nonavailability of ground truth HR images; 2) limitation of a large count of the unpaired dataset for deep neural network training.
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation
To deal with datasets from new domains a model needs to be trained from scratch.
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution
We show that the proposed method produces visually diverse and plausible results in multiple domains compared to the state-of-the-art methods.
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs).
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
In addition, we propose training a semantic segmentation network along with the translation task, and to leverage this output as a loss term that improves robustness.
Unsupervised Image to Image Translation for Multiple Retinal Pathology Synthesis in Optical Coherence Tomography Scans
To address this issue, we propose an unsupervised multi-domain I2I network with pre-trained style encoder that translates retinal OCT images in one domain to multiple domains.
