Image-to-Image Translation

256 papers with code • 31 benchmarks • 22 datasets

Image-to-image translation is the task of taking images from one domain and transforming them so they have the style (or characteristics) of images from another domain.

( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )

Greatest papers with code

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Breast Tumour Classification Domain Generalization +8

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

tensorflow/models 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.

 Ranked #1 on Image-to-Image Translation on photo2vangogh (Frechet Inception Distance metric)

Multimodal Unsupervised Image-To-Image Translation Style Transfer +1

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

eriklindernoren/PyTorch-GAN CVPR 2018

To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.

 Ranked #1 on Image-to-Image Translation on RaFD (using extra training data)

Image-to-Image Translation

DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

eriklindernoren/PyTorch-GAN ICCV 2017

Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.

Image-to-Image Translation

Unsupervised Image-to-Image Translation Networks

eriklindernoren/PyTorch-GAN 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.

Domain Adaptation Multimodal Unsupervised Image-To-Image Translation +1

Coupled Generative Adversarial Networks

eriklindernoren/PyTorch-GAN NeurIPS 2016

We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images.

Domain Adaptation Image-to-Image Translation

Semantic Image Synthesis with Spatially-Adaptive Normalization

NVlabs/SPADE CVPR 2019

Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers.

Image-to-Image Translation