Artistic style transfer aims to transfer the style of an artwork to a
photograph while maintaining its original overall content. Many prior works
focus on designing various transfer modules to transfer the style statistics to
the content image. Although effective, ignoring the clear disentanglement of
the content features and the style features from the first beginning, they have
difficulty in balancing between content preservation and style transferring. To
tackle this problem, we propose a novel information disentanglement method,
named InfoStyler, to capture the minimal sufficient information for both
content and style representations from the pre-trained encoding network.
InfoStyler formulates the disentanglement representation learning as an
information compression problem by eliminating style statistics from the
content image and removing the content structure from the style image. Besides,
to further facilitate disentanglement learning, a cross-domain Information
Bottleneck (IB) learning strategy is proposed by reconstructing the content and
style domains. Extensive experiments demonstrate that our InfoStyler can
synthesize high-quality stylized images while balancing content structure
preservation and style pattern richness.

我们提出了一种名为 InfoStyler 的新颖信息分解方法，通过消除内容图像中的样式统计数据和去除样式图像中的内容结构，捕获了内容和样式表示的最小充分信息。此外，为了进一步促进信息分解学习，我们提出了一种跨域信息瓶颈学习策略，通过重建内容和样式域来实现。大量实验证明，我们的 InfoStyler 可以在平衡内容结构保存和样式纹理丰富性的同时合成高质量的风格化图像。