Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.
( Image credit: MUNIT: Multimodal UNsupervised Image-to-image Translation )
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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
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.
#2 best model for Multimodal Unsupervised Image-To-Image Translation on Cats-and-Dogs
Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image.
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images.