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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Humans are capable of learning new concepts from only a few (labeled)exemplars, incrementally and continually. This happens within the context thatwe can differentiate among the exemplars, and between the exemplars and largeamounts of other data (unlabeled and labeled). This suggests, in humanlearning, supervised learning of concepts based on exemplars takes place withinthe larger context of contrastive self-supervised learning (CSSL) based onunlabeled and labeled data. We discuss extending CSSL (1) to be based mainly onexemplars and only secondly on data augmentation, and (2) to apply to bothunlabeled data (a large amount is available in general) and labeled data (a fewexemplars can be obtained with valuable supervised knowledge). A major benefitof the extensions is that exemplar-based CSSL, with supervised finetuning,supports few-shot class incremental learning (CIL). Specifically, we discussexemplar-based CSSL including: nearest-neighbor CSSL, neighborhood CSSL withsupervised pretraining, and exemplar CSSL with supervised finetuning. Wefurther discuss using exemplar-based CSSL to facilitate few-shot learning and,in particular, few-shot CIL.

Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning