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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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.
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
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