深度持续学习中的可塑性损失
Shibhansh Dohare1, J Fernando Hernandez-Garcia2, Qingfeng Lan2
1Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. dohare@ualberta.ca.
Nature
|August 21, 2024
概括
标准的深度学习方法在持续学习环境中失败,随着时间的推移而失去可塑性. 一个新的持续反向传播算法通过注入随机多样性来保持可塑性,这表明单独的梯度下降对于持续的深度学习是不够的.
科学领域:
- 人工智能
- 机器学习
- 深度学习
背景情况:
- 现代人工智能依赖于人工神经网络,深度学习和反向传播.
- 目前的方法通常使用不同的培训和评估阶段.
- 对于标准的深度学习来说,持续学习对于许多应用来说至关重要.
研究的目的:
- 在持续学习场景中研究标准深度学习方法的有效性.
- 确定当前深度学习方法的局限性,以实现持续适应.
- 开发和评估用于深度学习中保持可塑性的新算法.
主要方法:
- 在ImageNet上测试标准深度学习方法和强化学习任务.
- 在持续学习过程中分析深度网络的可塑性损失.
- 引入和评估随机单元重新启动的连续反向传播算法.
主要成果:
- 标准的深度学习方法在持续学习中逐渐丧失可塑性.
- 在没有具体干预的情况下,性能降低到浅层网络的水平.
- 连续反向传播算法成功地保持了无限期的可塑性.
结论:
- 基于梯度下降的方法不足以在持续的环境中进行深度学习.
- 通过诸如随机重新启动之类的机制注入多样性,
- 未来的深度学习需要混合方法,将基于梯度的学习与非梯度组件相结合.
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