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 )
We show the superiority of this method on fair classification and on textual style transfer tasks.
Stylised outputs are then obtained by computing similarities between both feature representations in order to transfer the style of the reference to the content of the target input.
Moreover, to alleviate the conflict between the targets of the conventional denoising procedure and the style transfer task, we propose another novel style denoising mechanism, which is more compatible with the target of the style transfer task.
We present a framework for conducting model extraction attacks against image translation models, and show that the adversary can successfully extract functional surrogate models.
Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis.
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image.