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

In the majority of GAN architectures, the latent space is defined as a set ofvectors of given dimensionality. Such representations are not easilyinterpretable and do not capture spatial information of image content directly.In this work, we define a family of spatial latent spaces for StyleGAN2,capable of capturing more details and representing images that areout-of-sample in terms of the number and arrangement of object parts, such asan image of multiple faces or a face with more than two eyes. We propose amethod for encoding images into our spaces, together with an attribute modelcapable of performing attribute editing in these spaces. We show that ourspaces are effective for image manipulation and encode semantic informationwell. Our approach can be used on pre-trained generator models, and attributeedition can be done using pre-generated direction vectors making the barrier toentry for experimentation and use extremely low. We propose a regularizationmethod for optimizing latent representations, which equalizes distributions ofparts of latent spaces, making representations much closer to generated ones.We use it for encoding images into spatial spaces to obtain significantimprovement in quality while keeping semantics and ability to use our attributemodel for edition purposes. In total, using our methods gives encoding qualityboost even as high as 30% in terms of LPIPS score comparing to standardmethods, while keeping semantics. Additionally, we propose a StyleGAN2 trainingprocedure on our spatial latent spaces, together with a custom spatial latentrepresentation distribution to make spatially closer elements in therepresentation more dependent on each other than farther elements. Suchapproach improves the FID score by 29% on SpaceNet, and is able to generateconsistent images of arbitrary sizes on spatially homogeneous datasets, likesatellite imagery.

Spatial Latent Representations in Generative Adversarial Networks for Image Generation