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

Supervised learning from training data with imbalanced class sizes, acommonly encountered scenario in real applications such as anomaly/frauddetection, has long been considered a significant challenge in machinelearning. Motivated by recent progress in curriculum and self-paced learning,we propose to adopt a semi-supervised learning paradigm by training a deepneural network, referred to as SelectNet, to selectively add unlabelled datatogether with their predicted labels to the training dataset. Unlike existingtechniques designed to tackle the difficulty in dealing with class imbalancedtraining data such as resampling, cost-sensitive learning, and margin-basedlearning, SelectNet provides an end-to-end approach for learning from importantunlabelled data "in the wild" that most likely belong to the under-sampledclasses in the training data, thus gradually mitigates the imbalance in thedata used for training the classifier. We demonstrate the efficacy of SelectNetthrough extensive numerical experiments on standard datasets in computervision.

SelectNet: Learning to Sample from the Wild for Imbalanced Data Training