English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Artistic style transfer aims to transfer the style of an artwork to aphotograph while maintaining its original overall content. Many prior worksfocus on designing various transfer modules to transfer the style statistics tothe content image. Although effective, ignoring the clear disentanglement ofthe content features and the style features from the first beginning, they havedifficulty in balancing between content preservation and style transferring. Totackle this problem, we propose a novel information disentanglement method,named InfoStyler, to capture the minimal sufficient information for bothcontent and style representations from the pre-trained encoding network.InfoStyler formulates the disentanglement representation learning as aninformation compression problem by eliminating style statistics from thecontent image and removing the content structure from the style image. Besides,to further facilitate disentanglement learning, a cross-domain InformationBottleneck (IB) learning strategy is proposed by reconstructing the content andstyle domains. Extensive experiments demonstrate that our InfoStyler cansynthesize high-quality stylized images while balancing content structurepreservation and style pattern richness.

InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer