Arbitrary style transfer is a significant topic with research value and
application prospect. A desired style transfer, given a content image and
referenced style painting, would render the content image with the color tone
and vivid stroke patterns of the style painting while synchronously maintaining
the detailed content structure information. Style transfer approaches would
initially learn content and style representations of the content and style
references and then generate the stylized images guided by these
representations. In this paper, we propose the multi-adaptation network which
involves two self-adaptation (SA) modules and one co-adaptation (CA) module:
the SA modules adaptively disentangle the content and style representations,
i.e., content SA module uses position-wise self-attention to enhance content
representation and style SA module uses channel-wise self-attention to enhance
style representation; the CA module rearranges the distribution of style
representation based on 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 and exact content structures to adapt to
various input images, respectively. Various qualitative and quantitative
experiments demonstrate that the proposed multi-adaptation network leads to
better results than the state-of-the-art style transfer methods.

本文提出了一种多适应网络，它包括两个自适应模块和一个协同适应模块。该模型使用自适应模块分离内容和风格表示，并使用一种新的解缠结损失函数来提取主要的风格模式和确切的内容结构，根据各种输入图像进行相应的调整，这样能够更好地进行样式转移。实验证明，与现有的样式转移方法相比，提出的多适应网络可获得更好的结果。