Deep neural networks have excelled in machine learning, particularly in
vision tasks, however, they often suffer from catastrophic forgetting when
learning new tasks sequentially. In this work, we propose Cobweb4V, a novel
visual classification approach that 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 the
proficiency of Cobweb4V in learning visual concepts, requiring less data to
achieve effective learning outcomes compared to traditional methods,
maintaining stable performance over time, and achieving commendable asymptotic
behavior, without catastrophic forgetting effects. These characteristics align
with learning strategies in human cognition, positioning Cobweb4V as a
promising alternative to neural network approaches.

该研究提出了 Cobweb4V，一种新颖的视觉分类方法，通过借鉴人类逐步学习新概念的方式，有效地学习视觉概念而不会出现灾难性遗忘效应，与人类认知的学习策略相符，是一种有前景的神经网络方法替代方案。