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 present fast, realistic image generation on high-resolution, multimodaldatasets using hierarchical variational autoencoders (VAEs) trained on adeterministic autoencoder's latent space. In this two-stage setup, theautoencoder compresses the image into its semantic features, which are thenmodeled with a deep VAE. With this method, the VAE avoids modeling thefine-grained details that constitute the majority of the image's code length,allowing it to focus on learning its structural components. We demonstrate theeffectiveness of our two-stage approach, achieving a FID of 9.34 on theImageNet-256 dataset which is comparable to BigGAN. We make our implementationavailable online.

High Fidelity Image Synthesis With Deep VAEs In Latent Space