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HiDe-PET:通过对参数有效调节的层次分解进行持续学习
概括
本研究介绍了层次分解PET (HiDe-PET),这是一个持续学习 (CL) 的新框架,通过优化分解目标来增强预训练模型 (PTM). HiDe-PET 提高了知识转移和模型在顺序任务中的弹性.
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:
- 预训练模型 (PTM) 对于持续学习 (CL) 至关重要,它提供知识转移并防止灾难性遗忘.
- 参数效率调整 (PET) 方法,特别是基于提示的方法,用于CL的冷PTM骨干,但往往产生低于最佳的结果.
研究的目的:
- 为CL与PTM和PET提出一个统一的框架,提供理论和经验方面的进步.
- 为了解决目前基于提示的PET技术在CL设置中的低于最佳性能.
主要方法:
- 在预培训环境中对CL目标进行理论分析,将其分解为任务内预测,任务身份推断和任务适应性预测.
- 引入了分层分解PET (HiDe-PET),通过PET技术结合特定任务和共享知识并恢复预训练的表示来优化分解的目标.
- 研究了实施策略,PET技术,PET架构以及在分销转换下适应性知识积累的影响.
主要成果:
- 与强大的基线相比,HiDe-PET在各种CL场景中表现出卓越的性能.
- 该框架提供了 CL 中实施策略,PET 技术和架构的独特影响的见解.
- 适应性知识积累在显著的分布变化中被证明是有效的.
结论:
- 提出的HiDe-PET框架提供了一个理论上有基础的和经验上有效的方法,用于使用预训练模型和参数效率调整进行持续学习.
- HiDe-PET通过优化分解目标,增强知识转移和连续学习任务中的弹性来推进该领域.
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