分布性稳健的记忆进化与通用分歧的持续学习
IEEE transactions on pattern analysis and machine intelligence
|September 22, 2023
概括
这项研究介绍了一种用于持续学习 (CL) 的新型记忆进化框架,以防止模型遗忘. 通过使用分布式稳定优化 (DRO),该框架增强了对对抗示例的概括性和稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 持续学习 (CL) 模型与非静态数据分布和知识遗忘作斗争.
- 在CL中现有的内存重播方法可能会过载存储的数据,限制概括.
- 当前的CL方法经常忽略内存数据分布中的不确定性.
研究的目的:
- 为持续学习提出一个原则性的记忆进化框架.
- 动态调整内存数据分布以提高模型性能.
- 为了增强在CL设置中的对抗性示例的稳定性.
主要方法:
- 采用分布性强大的优化 (DRO) 来动态演变内存缓冲区的分布.
- 在DRO中利用了f-分歧和瓦瑟斯坦球约束.
- 对于f-分歧约束的衍生瓦斯斯坦梯度流 (WGF) 方法.
- 开发了欧几里德空间中瓦瑟斯坦球约束的直接解决方案.
主要成果:
- 在广泛的基准实验中显著缓解忘记.
- 与现有的CL方法相比,展示了改进的泛化能力.
- 作为框架的副产品,观察到对抗性示例的增强稳定性.
结论:
- 提议的记忆进化框架有效地解决了在持续学习中遗忘的问题.
- 分布性强优化为管理内存数据提供了一个原则性的方法.
- 该框架提供了提高学习和对抗强度的双重好处.
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