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 show that the sum of the implicit generator log-density $\log p_g$ of aGAN with the logit score of the discriminator defines an energy function whichyields the true data density when the generator is imperfect but thediscriminator is optimal, thus making it possible to improve on the typicalgenerator (with implicit density $p_g$). To make that practical, we show thatsampling from this modified density can be achieved by sampling in latent spaceaccording to an energy-based model induced by the sum of the latent priorlog-density and the discriminator output score. This can be achieved by runninga Langevin MCMC in latent space and then applying the generator function, whichwe call Discriminator Driven Latent Sampling~(DDLS). We show that DDLS ishighly efficient compared to previous methods which work in thehigh-dimensional pixel space and can be applied to improve on previouslytrained GANs of many types. We evaluate DDLS on both synthetic and real-worlddatasets qualitatively and quantitatively. On CIFAR-10, DDLS substantiallyimproves the Inception Score of an off-the-shelf pre-trainedSN-GAN~\citep{sngan} from $8.22$ to $9.09$ which is even comparable to theclass-conditional BigGAN~\citep{biggan} model. This achieves a newstate-of-the-art in unconditional image synthesis setting without introducingextra parameters or additional training.

Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling