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/

Zendy Plus

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

Zendy Tools

Zendy Open

Maximum likelihood (ML) and adversarial learning are two popular approachesfor training generative models, and from many perspectives these techniques arecomplementary. ML learning encourages the capture of all data modes, and it istypically characterized by stable training. However, ML learning tends todistribute probability mass diffusely over the data space, $e.g.$, yieldingblurry synthetic images. Adversarial learning is well known to synthesizehighly realistic natural images, despite practical challenges like modedropping and delicate training. We propose an $\alpha$-Bridge to unify theadvantages of ML and adversarial learning, enabling the smooth transfer fromone to the other via the $\alpha$-divergence. We reveal that generalizations ofthe $\alpha$-Bridge are closely related to approaches developed recently toregularize adversarial learning, providing insights into that prior work, andfurther understanding of why the $\alpha$-Bridge performs well in practice.

Bridging Maximum Likelihood and Adversarial Learning via $\alpha$-Divergence