DILS:深度增量学习策略
Yanmei Wang1,2,3, Zhi Han1,2, Siquan Yu1,2
1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Frontiers in neurorobotics
|January 23, 2024
概括
我们引入了深度增量学习策略 (DILS),将知识从浅层传输到更深层的神经网络. 这种方法通过促进知识转移和加速对更大的模型的培训,使高效的系统升级成为可能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 现有的知识传输方法主要支持相同尺寸或更大到更小的网络传输.
- 当前的端到端培训未能在系统升级期间利用先前存在的浅层网络的知识.
- 这种限制阻碍了灵活性,并导致计算效率低下.
研究的目的:
- 开发一种用于将知识从浅层传输到深层神经网络的新方法.
- 为了提高性能,在网络深化过程中实现高效的知识继承.
- 解决当前培训策略在增量网络增长中的局限性.
主要方法:
- 提出一个深度增量学习策略 (DILS),包括逐渐插入层次.
- 导出用于训练新参数的分析和网络近似方法.
- 利用信息投影理论来确保知识的继承.
主要成果:
- DILS能够有效地将知识从较小的网络转移到较大的网络.
- 新的深层网络从浅层网络中继承知识.
- 通过有效的层初始化,为较大的模型提供稳定的性能和加速训练.
结论:
- 在深度增量网络学习中,DILS为知识转移提供了可行的解决方案.
- 该策略在合成和现实世界的实验中被证明是有效的.
- 促进高效的系统升级,减少计算浪费.
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