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

Generative Adversarial Networks (GANs) have had tremendous applications inComputer Vision. Yet, in the context of space science and planetary explorationthe door is open for major advances. We introduce tools to handle planetarydata from the mission Chang'E-4 and present a framework for Neural StyleTransfer using Cycle-consistency from rendered images. The experiments areconducted in the context of the Iris Lunar Rover, a nano-rover that will bedeployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being thefirst unmanned rover of America to be on the Moon.

Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4