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

Most modern unsupervised domain adaptation (UDA) approaches are rooted indomain alignment, i.e., learning to align source and target features to learn atarget domain classifier using source labels. In semi-supervised domainadaptation (SSDA), when the learner can access few target domain labels, priorapproaches have followed UDA theory to use domain alignment for learning. Weshow that the case of SSDA is different and a good target classifier can belearned without needing alignment. We use self-supervised pretraining (viarotation prediction) and consistency regularization to achieve well separatedtarget clusters, aiding in learning a low error target classifier. With ourPretraining and Consistency (PAC) approach, we achieve state of the art targetaccuracy on this semi-supervised domain adaptation task, surpassing multipleadversarial domain alignment methods, across multiple datasets. PAC, whileusing simple techniques, performs remarkably well on large and challenging SSDAbenchmarks like DomainNet and Visda-17, often outperforming recent state of theart by sizeable margins. Code for our experiments can be found athttps://github.com/venkatesh-saligrama/PAC

Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency