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Image-to-Image Translation

123 papers with code · Computer Vision
Subtask of Image Generation

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 )

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Greatest papers with code

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

ICCV 2017 tensorflow/models

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.

MULTIMODAL UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION STYLE TRANSFER UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Learning from Simulated and Unsupervised Images through Adversarial Training

CVPR 2017 tensorflow/models

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.

DOMAIN ADAPTATION GAZE ESTIMATION HAND POSE ESTIMATION IMAGE-TO-IMAGE TRANSLATION

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

ICCV 2017 eriklindernoren/Keras-GAN

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

IMAGE-TO-IMAGE TRANSLATION

Coupled Generative Adversarial Networks

NeurIPS 2016 eriklindernoren/Keras-GAN

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

DOMAIN ADAPTATION IMAGE-TO-IMAGE TRANSLATION

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

CVPR 2018 eriklindernoren/PyTorch-GAN

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.

 SOTA for Image-to-Image Translation on RaFD (using extra training data)

IMAGE-TO-IMAGE TRANSLATION

Unsupervised Image-to-Image Translation Networks

NeurIPS 2017 eriklindernoren/PyTorch-GAN

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 UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION

Semantic Image Synthesis with Spatially-Adaptive Normalization

CVPR 2019 NVlabs/SPADE

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