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

Recent advances in learning aligned multimodal representations have beenprimarily driven by training large neural networks on massive, noisypaired-modality datasets. In this work, we ask whether it is possible toachieve similar results with substantially less training time and data. Weachieve this by taking advantage of existing pretrained unimodal encoders andcareful curation of alignment data relevant to the downstream task of interest.We study a natural approach to aligning existing encoders via small auxiliaryfunctions, and we find that this method is competitive with (or outperforms)state of the art in many settings while being less prone to overfitting, lesscostly to train, and more robust to distribution shift. With a properly chosenalignment distribution, our method surpasses prior state of the art forImageNet zero-shot classification on public data while using two orders ofmagnitude less time and data and training 77% fewer parameters.

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations