通过进化实现深度持续学习
Achieving Deep Continual Learning via Evolution
AI总结：
针对深度神经网络持续学习能力受限的问题，受人类群体学习启发提出ECL框架，通过进化多样化模型群体、为每个增量任务定制专用专家模型，解决稳定性与可塑性矛盾，性能显著优于现有个体级持续学习方法，开辟AI持续学习新路径。
摘要：
深度神经网络尽管取得了显著成功，但其执行持续学习（Continual Learning，CL）的能力仍存在根本性局限。当前大多数方法旨在提升单个模型的能力，受人类群体集体学习机制的启发，我们提出了进化持续学习（Evolving Continual Learning，ECL）框架，该框架维护并进化一个由多样化神经网络模型组成的群体。ECL会为每个引入的增量任务持续搜索最优架构。这种定制化模型在对应任务上进行训练，并作为专用专家模型被归档，为不断增长的技能集合添砖加瓦。该方法从本质上解决了持续学习的核心挑战：稳定性通过专家模型的隔离得以实现，而可塑性则通过进化出独特的、任务专用的架构得到极大增强。实验结果表明，ECL的性能显著优于当前最先进的个体层面持续学习方法。通过将关注点从个体适应转向集体进化，ECL为构建具备持续学习能力的AI系统提供了一条新路径。
英文摘要：
Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance the capabilities of a single model, Inspired by the collective learning mechanisms of human populations, we introduce Evolving Continual Learning (ECL), a framework that maintains and evolves a diverse population of neural network models. ECL continually searches for an optimal architecture for each introduced incremental task. This tailored model is trained on the corresponding task and archived as a specialized expert, contributing to a growing collection of skills. This approach inherently resolves the core CL challenges: stability is achieved through the isolation of expert models, while plasticity is greatly enhanced by evolving unique, task-specific architectures. Experimental results demonstrate that ECL significantly outperforms state-of-the-art individual-level CL methods. By shifting the focus from individual adaptation to collective evolution, ECL presents a novel path toward AI systems capable of CL.
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。
