  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.
