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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present HandGAN (H-GAN), a cycle-consistent adversarial learning approachimplementing multi-scale perceptual discriminators. It is designed to translatesynthetic images of hands to the real domain. Synthetic hands provide completeground-truth annotations, yet they are not representative of the targetdistribution of real-world data. We strive to provide the perfect blend of arealistic hand appearance with synthetic annotations. Relying on image-to-imagetranslation, we improve the appearance of synthetic hands to approximate thestatistical distribution underlying a collection of real images of hands. H-GANtackles not only the cross-domain tone mapping but also structural differencesin localized areas such as shading discontinuities. Results are evaluated on aqualitative and quantitative basis improving previous works. Furthermore, werelied on the hand classification task to claim our generated hands arestatistically similar to the real domain of hands.

H-GAN: the power of GANs in your Hands