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

Noisy-labels are challenging for deep learning due to the high capacity ofthe deep models that can overfit noisy-label training samples. Arguably themost realistic and coincidentally challenging type of label noise is theinstance-dependent noise (IDN), where the labelling errors are caused by theambivalent information present in the images. The most successful label noiselearning techniques to address IDN problems usually contain a noisy-labelsample selection stage to separate clean and noisy-label samples duringtraining. Such sample selection depends on a criterion, such as loss orgradient, and on a curriculum to define the proportion of training samples tobe classified as clean at each training epoch.  Even though the estimated noise rate from the training set appears to be anatural signal to be used in the definition of this curriculum, previousapproaches generally rely on arbitrary thresholds or pre-defined selectionfunctions to the best of our knowledge. This paper addresses this research gapby proposing a new noisy-label learning graphical model that can easilyaccommodate state-of-the-art (SOTA) noisy-label learning methods and providethem with a reliable noise rate estimate to be used in a new sample selectioncurriculum. We show empirically that our model integrated with many SOTAmethods can improve their results in many IDN benchmarks, including syntheticand real-world datasets.

Noisy-label Learning with Sample Selection based on Noise Rate Estimate