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 underlying geometrical structure of the latent space in deep generativemodels is in most cases not Euclidean, which may lead to biases when comparinginterpolation capabilities of two models. Smoothness and plausibility of linearinterpolations in latent space are associated with the quality of theunderlying generative model. In this paper, we show that not all suchinterpolations are comparable as they can deviate arbitrarily from the shortestinterpolation curve given by the geodesic. This deviation is revealed bycomputing curve lengths with the pull-back metric of the generative model,finding shorter curves than the straight line between endpoints, and measuringa non-zero relative length improvement on this straight line. This leads to astrategy to compare linear interpolations across two generative models. We alsoshow the effect and importance of choosing an appropriate output space forcomputing shorter curves. For this computation we derive an extension of thepull-back metric.

On Linear Interpolation in the Latent Space of Deep Generative Models