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 rapid advancement of Generative Adversarial Networks (GANs) necessitatesthe need to robustly evaluate these models. Among the established evaluationcriteria, the Fr\'{e}chet Inception Distance (FID) has been widely adopted dueto its conceptual simplicity, fast computation time, and strong correlationwith human perception. However, FID has inherent limitations, mainly stemmingfrom its assumption that feature embeddings follow a Gaussian distribution, andtherefore can be defined by their first two moments. As this does not hold inpractice, in this paper we explore the importance of third-moments in imagefeature data and use this information to define a new measure, which we callthe Skew Inception Distance (SID). We prove that SID is a pseudometric onprobability distributions, show how it extends FID, and present a practicalmethod for its computation. Our numerical experiments support that SID eithertracks with FID or, in some cases, aligns more closely with human perceptionwhen evaluating image features of ImageNet data.

Using Higher-Order Moments to Assess the Quality of GAN-generated Image Features