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 Unbalanced GANs, which pre-trains the generator of the generativeadversarial network (GAN) using variational autoencoder (VAE). We guarantee thestable training of the generator by preventing the faster convergence of thediscriminator at early epochs. Furthermore, we balance between the generatorand the discriminator at early epochs and thus maintain the stabilized trainingof GANs. We apply Unbalanced GANs to well known public datasets and find thatUnbalanced GANs reduce mode collapses. We also show that Unbalanced GANsoutperform ordinary GANs in terms of stabilized learning, faster convergenceand better image quality at early epochs.

Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder