446 papers with code • 2 benchmarks • 16 datasets
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.
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer.
In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair.
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.