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

Online continual learning (CL) aims to learn new knowledge and consolidatepreviously learned knowledge from non-stationary data streams. Due to thetime-varying training setting, the model learned from a changing distributioneasily forgets the previously learned knowledge and biases toward the newlyreceived task. To address this problem, we propose a Continual Bias Adaptor(CBA) module to augment the classifier network to adapt to catastrophicdistribution change during training, such that the classifier network is ableto learn a stable consolidation of previously learned tasks. In the testingstage, CBA can be removed which introduces no additional computation cost andmemory overhead. We theoretically reveal the reason why the proposed method caneffectively alleviate catastrophic distribution shifts, and empiricallydemonstrate its effectiveness through extensive experiments based on fourrehearsal-based baselines and three public continual learning benchmarks.

CBA: Improving Online Continual Learning via Continual Bias Adaptor