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

Universal domain adaptation (UniDA) is a practical but challenging problem,in which information about the relation between the source and the targetdomains is not given for knowledge transfer. Existing UniDA methods may sufferfrom the problems of overlooking intra-domain variations in the target domainand difficulty in separating between the similar known and unknown class. Toaddress these issues, we propose a novel Mutual Learning Network (MLNet) withneighborhood invariance for UniDA. In our method, confidence-guided invariantfeature learning with self-adaptive neighbor selection is designed to reducethe intra-domain variations for more generalizable feature representation. Byusing the cross-domain mixup scheme for better unknown-class identification,the proposed method compensates for the misidentified known-class errors bymutual learning between the closed-set and open-set classifiers. Extensiveexperiments on three publicly available benchmarks demonstrate that our methodachieves the best results compared to the state-of-the-arts in most cases andsignificantly outperforms the baseline across all the four settings in UniDA.Code is available at https://github.com/YanzuoLu/MLNet.

MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation