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

The purpose of the research is to determine if currently availableself-supervised learning techniques can accomplish human level comprehension ofvisual images using the same degree and amount of sensory input that peopleacquire from. Initial research on this topic solely considered data volumescaling. Here, we scale both the volume of data and the quality of the image.This scaling experiment is a self-supervised learning method that may be donewithout any outside financing. We find that scaling up data volume and pictureresolution at the same time enables human-level item detection performance atsub-human sizes.We run a scaling experiment with vision transformers trained onup to 2 images up to 256 ppi.

A degree of image identification at sub-human scales could be possible with more advanced clusters