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

Pretraining methods are typically compared by evaluating the accuracy oflinear classifiers, transfer learning performance, or visually inspecting therepresentation manifold's (RM) lower-dimensional projections. We show that thedifferences between methods can be understood more clearly by investigating theRM directly, which allows for a more detailed comparison. To this end, wepropose a framework and new metric to measure and compare different RMs. Wealso investigate and report on the RM characteristics for various pretrainingmethods. These characteristics are measured by applying sequentially largerlocal alterations to the input data, using white noise injections and ProjectedGradient Descent (PGD) adversarial attacks, and then tracking each datapoint.We calculate the total distance moved for each datapoint and the relativechange in distance between successive alterations. We show that self-supervisedmethods learn an RM where alterations lead to large but constant size changes,indicating a smoother RM than fully supervised methods. We then combine thesemeasurements into one metric, the Representation Manifold Quality Metric(RMQM), where larger values indicate larger and less variable step sizes, andshow that RMQM correlates positively with performance on downstream tasks.

Manifold Characteristics That Predict Downstream Task Performance