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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Generating high fidelity identity-preserving faces with different facialattributes has a wide range of applications. Although a number of generativemodels have been developed to tackle this problem, there is still much room forfurther improvement.In paticular, the current solutions usually ignore theperceptual information of images, which we argue that it benefits the output ofa high-quality image while preserving the identity information, especially infacial attributes learning area.To this end, we propose to train GANiteratively via regularizing the min-max process with an integrated loss, whichincludes not only the per-pixel loss but also the perceptual loss. In contrastto the existing methods only deal with either image generation ortransformation, our proposed iterative architecture can achieve both of them.Experiments on the multi-label facial dataset CelebA demonstrate that theproposed model has excellent performance on recognizing multiple attributes,generating a high-quality image, and transforming image with controllableattributes.

Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN