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 models often use human evaluations to measure the perceivedquality of their outputs. Automated metrics are noisy indirect proxies, becausethey rely on heuristics or pretrained embeddings. However, up until now, directhuman evaluation strategies have been ad-hoc, neither standardized norvalidated. Our work establishes a gold standard human benchmark for generativerealism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmarkthat is (1) grounded in psychophysics research in perception, (2) reliableacross different sets of randomly sampled outputs from a model, (3) able toproduce separable model performances, and (4) efficient in cost and time. Weintroduce two variants: one that measures visual perception under adaptive timeconstraints to determine the threshold at which a model's outputs appear real(e.g. 250ms), and the other a less expensive variant that measures human errorrate on fake and real images sans time constraints. We test HYPE across sixstate-of-the-art generative adversarial networks and two sampling techniques onconditional and unconditional image generation using four datasets: CelebA,FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvementsacross training epochs, and we confirm via bootstrap sampling that HYPErankings are consistent and replicable.

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models