Image-to-Image Translation
432 papers with code • 37 benchmarks • 29 datasets
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )
Benchmarks
These leaderboards are used to track progress in Image-to-Image Translation
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Libraries
Use these libraries to find Image-to-Image Translation models and implementationsDatasets
Cityscapes
KITTI
ADE20K
CelebA-HQ
SYNTHIA
GTA5
DeepFashion
Perceptual Similarity
AFHQ
COCO-Stuff
Subtasks
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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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.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
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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.
Image-to-Image Translation with Conditional Adversarial Networks
phillipi/pix2pix
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CVPR 2017
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
eriklindernoren/PyTorch-GAN
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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.
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
Semantic Image Synthesis with Spatially-Adaptive Normalization
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.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
NVIDIA/pix2pixHD
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CVPR 2018
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
Learning to Adapt Structured Output Space for Semantic Segmentation
wasidennis/AdaptSegNet
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CVPR 2018
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Multimodal Unsupervised Image-to-Image Translation
To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain.
Everybody Dance Now
carolineec/EverybodyDanceNow
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ICCV 2019
This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
