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 Multimodal Unsupervised Image-To-Image Translation on EPFL NIR-VIS
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.
Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.
#2 best model for Image-to-Image Translation on Aerial-to-Map
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.
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.
Our proposed method encourages bijective consistency between the latent encoding and output modes.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Edge-to-Shoes
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)
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