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 majority of computer vision algorithms fail to find higher-order(abstract) patterns in an image so are not robust against adversarial attacks,unlike human lateralized vision. Deep learning considers each input pixel in ahomogeneous manner such that different parts of a ``locality-sensitive hashingtable'' are often not connected, meaning higher-order patterns are notdiscovered. Hence these systems are not robust against noisy, irrelevant, andredundant data, resulting in the wrong prediction being made with highconfidence. Conversely, vertebrate brains afford heterogeneous knowledgerepresentation through lateralization, enabling modular learning at differentlevels of abstraction. This work aims to verify the effectiveness, scalability,and robustness of a lateralized approach to real-world problems that containnoisy, irrelevant, and redundant data. The experimental results of multi-class(200 classes) image classification show that the novel system effectivelylearns knowledge representation at multiple levels of abstraction making itmore robust than other state-of-the-art techniques. Crucially, the novellateralized system outperformed all the state-of-the-art deep learning-basedsystems for the classification of normal and adversarial images by 19.05% -41.02% and 1.36% - 49.22%, respectively. Findings demonstrate the value ofheterogeneous and lateralized learning for computer vision applications.

Lateralized Learning for Multi-Class Visual Classification Tasks