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/

Zendy Plus

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

Zendy Tools

Zendy Open

Deep learning faces a formidable challenge when handling noisy labels, asmodels tend to overfit samples affected by label noise. This challenge isfurther compounded by the presence of instance-dependent noise (IDN), arealistic form of label noise arising from ambiguous sample information. Toaddress IDN, Label Noise Learning (LNL) incorporates a sample selection stageto differentiate clean and noisy-label samples. This stage uses an arbitrarycriterion and a pre-defined curriculum that initially selects most samples asnoisy and gradually decreases this selection rate during training. Suchcurriculum is sub-optimal since it does not consider the actual label noiserate in the training set. This paper addresses this issue with a new noise-rateestimation method that is easily integrated with most state-of-the-art (SOTA)LNL methods to produce a more effective curriculum. Synthetic and real-worldbenchmark results demonstrate that integrating our approach with SOTA LNLmethods improves accuracy in most cases.

Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation