使用可逆生成模型进行持续学习
Jary Pomponi1, Simone Scardapane1, Aurelio Uncini1
1Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Italy.
概括
这项研究引入了一种新方法,通过结合规范化和生成排练来防止神经网络中的灾难性遗忘. 该方法使用规范化流程来有效地维护过去的知识,优于现有技术.
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:
- 灾难性遗忘 (CF) 是持续学习的一个主要挑战,在持续学习中,神经网络在接受新任务训练时会覆盖先前获得的知识.
- 减轻CF现有的方法包括体重调节和排练策略,使用生成模型创建用于排练的合成数据.
研究的目的:
- 提出一种新的方法,将规范化和基于生成的排练集成在一起,以对抗神经网络中的灾难性遗忘.
- 在整个培训过程中开发一种有效的记忆方法,在整个培训过程中保持持续的记忆开销.
主要方法:
- 一个基于规范化流 (NF) 的生成模型,一个概率和可逆的神经网络,在神经网络的内部嵌入上进行训练.
- 通过利用NF的可逆性,实现了对网络嵌入有关过去任务的简单规范化技术.
- 提出的方法结合了规范化和基于生成的排练策略的优势.
主要成果:
- 与文献中最先进的方法相比,提出的方法表现良好.
- 通过在整个训练过程中使用单个NF,将内存开销保持在恒定的水平.
- 该方法通过有限的计算能力和内存要求来实现这一目标.
结论:
- 这种新的方法有效地解决了灾难性遗忘,通过协同结合规范化和生成排练.
- 使用规范化流程为持续学习提供了高效和可扩展的解决方案.
- 这种技术为开发更强大,更持久的神经网络模型提供了有前途的方向.
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