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

In this paper, we address the challenges of online Continual Learning (CL) byintroducing a density distribution-based learning framework. CL, especially theClass Incremental Learning, enables adaptation to new test distributions whilecontinuously learning from a single-pass training data stream, which is more inline with the practical application requirements of real-world scenarios.However, existing CL methods often suffer from catastrophic forgetting andhigher computing costs due to complex algorithm designs, limiting theirpractical use. Our proposed framework overcomes these limitations by achievingsuperior average accuracy and time-space efficiency, bridging the performancegap between CL and classical machine learning. Specifically, we adopt anindependent Generative Kernel Density Estimation (GKDE) model for each CL task.During the testing stage, the GKDEs utilize a self-reported max probabilitydensity value to determine which one is responsible for predicting incomingtest instances. A GKDE-based learning objective can ensure that samples withthe same label are grouped together, while dissimilar instances are pushedfarther apart. Extensive experiments conducted on multiple CL datasets validatethe effectiveness of our proposed framework. Our method outperforms popular CLapproaches by a significant margin, while maintaining competitive time-spaceefficiency, making our framework suitable for real-world applications. Codewill be available at https://github.com/xxxx/xxxx.

Density Distribution-based Learning Framework for Addressing Online Continual Learning Challenges