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.

翻译：深度神经网络在机器学习领域表现出色，尤其在视觉任务中，然而，它们在学习新任务序列时常常遭受灾难性遗忘。在本工作中，我们提出Cobweb4V，一种新颖的视觉分类方法，它建立在Cobweb之上，而Cobweb是一种受人类随时间增量学习新概念方式启发的人类化学习系统。本研究中，我们进行了全面评估，展示了Cobweb4V在学习视觉概念方面的能力，与传统方法相比，它需要更少的数据即可达到有效的学习效果，随时间保持稳定的性能，并实现令人满意的渐近行为，而无灾难性遗忘效应。这些特性与人类认知中的学习策略相一致，使Cobweb4V成为神经网络方法的一种有前景的替代方案。