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. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss.
#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. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions.
#4 best model for Image-to-Image Translation on Cityscapes Labels-to-Photo
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping.
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.
Recent studies have shown remarkable success in image-to-image translation for two domains. 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 present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic.
Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. 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.
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. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Cats-and-Dogs
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged.
#2 best model for Unsupervised Image-To-Image Translation on SVNH-to-MNIST
Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.