Arbitrary Style Transfer via Multi-Adaptation Network

Arbitrary style transfer is a significant topic with both research value and application prospect.Given a content image and a referenced style painting, a desired style transfer would render the content image with the color tone and vivid stroke patterns of the style painting while synchronously maintain the detailed content structure information.Commonly, style transfer approaches would learn content and style representations of the content and style references first and then generate the stylized images guided by these representations.In this paper, we propose the multi-adaption network which involves two Self-Adaptation (SA) modules and one Co-Adaptation (CA) module: SA modules adaptively disentangles the content and style representations, i.e., content SA module uses the position-wise self-attention to enhance content representation and style SA module uses channel-wise self-attention to enhance style representation; CA module rearranges the distribution of style representation according to content representation distribution by calculating the local similarity between the disentangled content and style features in a non-local fashion.Moreover, a new disentanglement loss function enables our network to extract main style patterns to adapt to various content images and extract exact content features to adapt to various style images. Various qualitative and quantitative experiments demonstrate that the proposed multi-adaption network leads to better results than the state-of-the-art style transfer methods.