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

The integrity of training data, even when annotated by experts, is far fromguaranteed, especially for non-IID datasets comprising both in- andout-of-distribution samples. In an ideal scenario, the majority of sampleswould be in-distribution, while samples that deviate semantically would beidentified as out-of-distribution and excluded during the annotation process.However, experts may erroneously classify these out-of-distribution samples asin-distribution, assigning them labels that are inherently unreliable. Thismixture of unreliable labels and varied data types makes the task of learningrobust neural networks notably challenging. We observe that both in- andout-of-distribution samples can almost invariably be ruled out from belongingto certain classes, aside from those corresponding to unreliable ground-truthlabels. This opens the possibility of utilizing reliable complementary labelsthat indicate the classes to which a sample does not belong. Guided by thisinsight, we introduce a novel approach, termed \textit{Gray Learning} (GL),which leverages both ground-truth and complementary labels. Crucially, GLadaptively adjusts the loss weights for these two label types based onprediction confidence levels. By grounding our approach in statistical learningtheory, we derive bounds for the generalization error, demonstrating that GLachieves tight constraints even in non-IID settings. Extensive experimentalevaluations reveal that our method significantly outperforms alternativeapproaches grounded in robust statistics.

Gray Learning from Non-IID Data with Out-of-distribution Samples