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

Label noise is frequently observed in real-world large-scale datasets. Thenoise is introduced due to a variety of reasons; it is heterogeneous andfeature-dependent. Most existing approaches to handling noisy labels fall intotwo categories: they either assume an ideal feature-independent noise, orremain heuristic without theoretical guarantees. In this paper, we propose totarget a new family of feature-dependent label noise, which is much moregeneral than commonly used i.i.d. label noise and encompasses a broad spectrumof noise patterns. Focusing on this general noise family, we propose aprogressive label correction algorithm that iteratively corrects labels andrefines the model. We provide theoretical guarantees showing that for a widevariety of (unknown) noise patterns, a classifier trained with this strategyconverges to be consistent with the Bayes classifier. In experiments, ourmethod outperforms SOTA baselines and is robust to various noise types andlevels.

Learning with Feature-Dependent Label Noise: A Progressive Approach