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 or GANs are a type of generative modelingframework. GANs involve a pair of neural networks engaged in a competition initeratively creating fake data, indistinguishable from the real data. Onenotable application of GANs is developing fake human faces, also known as "deepfakes," due to the deep learning algorithms at the core of the GAN framework.Measuring the quality of the generated images is inherently subjective butattempts to objectify quality using standardized metrics have been made. Oneexample of objective metrics is the Frechet Inception Distance (FID), whichmeasures the difference between distributions of feature vectors for twoseparate datasets of images. There are situations that images with lowperceptual qualities are not assigned appropriate FID scores. We propose toimprove the robustness of the evaluation process by integrating lower-levelfeatures to cover a wider array of visual defects. Our proposed methodintegrates three levels of feature abstractions to evaluate the quality ofgenerated images. Experimental evaluations show better performance of theproposed method for distorted images.

Compound Frechet Inception Distance for Quality Assessment of GAN Created Images