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

We introduce an inversion based method, denoted as IMAge-Guided modelINvErsion (IMAGINE), to generate high-quality and diverse images from only asingle training sample. We leverage the knowledge of image semantics from apre-trained classifier to achieve plausible generations via matchingmulti-level feature representations in the classifier, associated withadversarial training with an external discriminator. IMAGINE enables thesynthesis procedure to simultaneously 1) enforce semantic specificityconstraints during the synthesis, 2) produce realistic images without generatortraining, and 3) give users intuitive control over the generation process. Withextensive experimental results, we demonstrate qualitatively and quantitativelythat IMAGINE performs favorably against state-of-the-art GAN-based andinversion-based methods, across three different image domains (i.e., objects,scenes, and textures).

IMAGINE: Image Synthesis by Image-Guided Model Inversion