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.
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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.
#2 best model for Image-to-Image Translation on Cityscapes Photo-to-Labels
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
#4 best model for Image-to-Image Translation on Cityscapes Photo-to-Labels
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
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
We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
#2 best model for Image-to-Image Translation on ADE20K-Outdoor Labels-to-Photos
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.
We study the problem of transferring a sample in one domain to an analog sample in another domain.
#2 best model for Unsupervised Image-To-Image Translation on SVNH-to-MNIST
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.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Cats-and-Dogs
To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.