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.
Ranked #2 on Pedestrian Attribute Recognition on UAV-Human
DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PEDESTRIAN ATTRIBUTE RECOGNITION PEDESTRIAN TRAJECTORY PREDICTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC 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.
Ranked #1 on Image-to-Image Translation on photo2vangogh (Frechet Inception Distance metric)
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
Ranked #1 on 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.
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.
Ranked #1 on Image-to-Image Translation on RaFD (using extra training data)
Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.
Ranked #2 on Image-to-Image Translation on Aerial-to-Map
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.