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

Semi-supervised learning (SSL) has played an important role in leveragingunlabeled data when labeled data is limited. One of the most successful SSLapproaches is based on consistency regularization, which encourages the modelto produce unchanged with perturbed input. However, there has been lessattention spent on inputs that have the same label. Motivated by theobservation that the inputs having the same label should have the similar modeloutputs, we propose a novel method, RankingMatch, that considers not only theperturbed inputs but also the similarity among the inputs having the samelabel. We especially introduce a new objective function, dubbed BatchMeanTriplet loss, which has the advantage of computational efficiency while takinginto account all input samples. Our RankingMatch achieves state-of-the-artperformance across many standard SSL benchmarks with a variety of labeled dataamounts, including 95.13% accuracy on CIFAR-10 with 250 labels, 77.65% accuracyon CIFAR-100 with 10000 labels, 97.76% accuracy on SVHN with 250 labels, and97.77% accuracy on SVHN with 1000 labels. We also perform an ablation study toprove the efficacy of the proposed BatchMean Triplet loss against existingversions of Triplet loss.

RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss