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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Generative adversarial networks (GANs) are capable of generating strikinglyrealistic samples but state-of-the-art GANs can be extremely computationallyexpensive to train. In this paper, we propose the fused propagation (FusedProp)algorithm which can be used to efficiently train the discriminator and thegenerator of common GANs simultaneously using only one forward and one backwardpropagation. We show that FusedProp achieves 1.49 times the training speedcompared to the conventional training of GANs, although further studies arerequired to improve its stability. By reporting our preliminary results andopen-sourcing our implementation, we hope to accelerate future research on thetraining of GANs.

FusedProp: Towards Efficient Training of Generative Adversarial Networks