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

Incorporating additional knowledge in the learning process can be beneficialfor several computer vision and machine learning tasks. Whether privilegedinformation originates from a source domain that is adapted to a target domain,or as additional features available at training time only, using suchprivileged (i.e., auxiliary) information is of high importance as it improvesthe recognition performance and generalization. However, both primary andprivileged information are rarely derived from the same distribution, whichposes an additional challenge to the recognition task. To address thesechallenges, we present a novel learning paradigm that leverages privilegedinformation in a domain adaptation setup to perform visual recognition tasks.The proposed framework, named Adaptive SVM+, combines the advantages of boththe learning using privileged information (LUPI) paradigm and the domainadaptation framework, which are naturally embedded in the objective function ofa regular SVM. We demonstrate the effectiveness of our approach on the publiclyavailable Animals with Attributes and INTERACT datasets and reportstate-of-the-art results in both of them.

Adaptive SVM+: Learning with Privileged Information for Domain Adaptation