Style transfer is the task of changing the style of an image in one domain to the style of an image in another domain.
( Image credit: A Neural Algorithm of Artistic Style )
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
MULTIMODAL UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION STYLE TRANSFER UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image.
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.
We consider image transformation problems, where an input image is transformed into an output image.
#5 best model for
Nuclear Segmentation
on Cell17
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
It this paper we revisit the fast stylization method introduced in Ulyanov et.
This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.).
We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence.
We first propose a taxonomy of current algorithms in the field of NST.
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example.