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 propose a new multimodal variational autoencoder that enables to generatefrom the joint distribution and conditionally to any number of complexmodalities. The unimodal posteriors are conditioned on the Deep CanonicalCorrelation Analysis embeddings which preserve the shared information acrossmodalities leading to more coherent cross-modal generations. Furthermore, weuse Normalizing Flows to enrich the unimodal posteriors and achieve morediverse data generation. Finally, we propose to use a Product of Experts forinferring one modality from several others which makes the model scalable toany number of modalities. We demonstrate that our method improves likelihoodestimates, diversity of the generations and in particular coherence metrics inthe conditional generations on several datasets.

Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis