视觉图像上的增量概念形成，无灾难性遗忘
Incremental Concept Formation over Visual Images Without Catastrophic Forgetting
AI总结：
针对深度神经网络在顺序学习中的灾难性遗忘问题，提出基于类人学习系统Cobweb的Cobweb4V视觉分类方法，以更少数据实现稳定且无遗忘的增量概念学习。
摘要：
深度神经网络在机器学习中表现出色，尤其在视觉任务中，然而，当顺序学习新任务时，它们常常遭受灾难性遗忘。在本工作中，我们引入了Cobweb4V，作为传统神经网络方法的一种替代方案。Cobweb4V是一种新颖的视觉分类方法，它建立在Cobweb之上，Cobweb是一种类人学习系统，其灵感来自于人类随时间增量学习新概念的方式。在本研究中，我们进行了全面评估，展示了Cobweb4V在学习视觉概念方面的熟练度，与传统方法相比，它需要更少的数据即可实现有效的学习成果，随时间保持稳定的性能，并达到值得称赞的渐近行为，且没有灾难性遗忘效应。这些特征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个有前景的替代方案。
英文摘要：
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 introduce Cobweb4V, an alternative to traditional neural network approaches. Cobweb4V is a novel visual classification method 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 Cobweb4Vs proficiency 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
