发现神经网络在渐变环境中能保持更强学习能力
Do Neural Networks Lose Plasticity in a Gradually Changing World?
- 通过渐进式输入输出插值模拟持续变化环境
- 任务切换越平缓，模型遗忘现象越轻
- 适合研究持续学习与现实世界适应性的学者
持续学习已成为机器学习的热门方向。近期研究发现一种称为‘可塑性丧失’的现象：神经网络在不断学习新任务时逐渐失去学习能力。然而，现有研究多基于任务切换突变的基准测试，未考察突变本身是否加剧了这种丧失。本文通过输入/输出插值和任务采样，模拟渐变环境，进行理论与实证分析，结果表明：可塑性丧失的严重程度与任务切换的突变程度密切相关；当环境渐变时，该问题可显著缓解。
原文摘要 · Abstract (English)
Continual learning has become a trending topic in machine learning. Recent studies have discovered an interesting phenomenon called loss of plasticity, referring to neural networks gradually losing the ability to learn new tasks. However, existing plasticity research largely relies on benchmarks with abrupt task transitions, without examining whether the abruptness itself contributes to the observed plasticity loss. In this paper, we investigate the role of transition abruptness by simulating gradually changing environments through input/output interpolation and task sampling. We perform theoretical and empirical analysis, showing that the severity of plasticity loss is closely tied to the abruptness of task transitions, and can be substantially reduced when the environment changes gradually.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。