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Robust incremental learning of visual concepts without catastrophic forgetting
Journal article   Open access   Peer reviewed

Robust incremental learning of visual concepts without catastrophic forgetting

Nicki Barari, Xin Lin and Christopher J. MacLellan
Cognitive systems research, v 96, 101447
Apr 2026
DOI: https://doi.org/10.1016/j.cogsys.2026.101447
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.cogsys.2026.101447View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2026CC BY V4.0,  Open

Abstract

Incremental concept learning Catastrophic forgetting
Continual learning poses a significant challenge in machine learning, as models often struggle to retain previously learned knowledge when exposed to new data, leading to catastrophic forgetting. In this work, we introduce Cobweb/4V, a novel visual classification method. This approach builds on Cobweb, a human-like learning system that is inspired by the way humans incrementally learn new concepts over time. In this research, we conduct a comprehensive evaluation showcasing Cobweb/4V’s proficiency in learning visual concepts, requiring less data to achieve effective learning outcomes compared to neural network approaches, maintaining stable performance over time, achieving competitive asymptotic behavior, and avoiding catastrophic forgetting. These characteristics align with human learning capabilities, positioning Cobweb/4V as a promising approach for sequential learning and motivating future exploration into its potential to guide the development of neural networks and other machine learning approaches that handle continual learning.

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