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

Semi-supervised learning (SSL) has seen great strides when labeled data isscarce but unlabeled data is abundant. Critically, most recent work assume thatsuch unlabeled data is drawn from the same distribution as the labeled data. Inthis work, we show that state-of-the-art SSL algorithms suffer a degradation inperformance in the presence of unlabeled auxiliary data that does notnecessarily possess the same class distribution as the labeled set. We termthis problem as Auxiliary-SSL and propose AuxMix, an algorithm that leveragesself-supervised learning tasks to learn generic features in order to maskauxiliary data that are not semantically similar to the labeled set. We alsopropose to regularize learning by maximizing the predicted entropy fordissimilar auxiliary samples. We show an improvement of 5% over existingbaselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeledsamples and all unlabeled data is drawn from the Tiny-ImageNet dataset. Wereport competitive results on several datasets and conduct ablation studies.

AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data