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通过知识脱和双重增强对比学习来缓解持续学习的不平衡
IEEE transactions on neural networks and learning systems
|January 8, 2024
概括
本研究介绍了持续学习 (CL) 的不平衡缓解框架,以防止灾难性遗忘. 新方法使用脱的知识蒸和双重增强的对比学习来保持旧知识并提高特征的独特性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 持续学习 (CL) 使模型能够从数据流中学习,而不会忘记过去的知识.
- 灾难性遗忘,在学习新任务后,旧任务的性能下降,是CL的一个主要挑战.
- 现有的基于重复的方法存在数据不平衡,导致信息抑制和弱特征可辨别性.
研究的目的:
- 在持续学习中解决灾难性遗忘问题.
- 为了减轻信息压制和弱特征歧视性问题,由CL中的数据不平衡引起.
- 提出一个新的框架,以提高知识的保留和特征在持续学习中的代表性.
主要方法:
- 为CL (IMCL) 框架开发了一个减轻不平衡的方法.
- 集成的脱知识蒸 (DKD) 减轻新任务抑制旧任务知识.
- 综合双重增强对比学习 (DECL) 改进低级和高级特征增强和任务区分.
主要成果:
- 通过脱模型输出概率,DKD方法有效地保持了旧的任务知识.
- DECL方法增强了特征表示,提高了旧任务和新任务之间的区分能力.
- 实验表明,在任务增量,类增量和域增量学习设置中,表现有希望.
结论:
- 拟议的IMCL框架有效地打击了持续学习中的灾难性遗忘.
- DKD和DECL的组合为CL中的数据不平衡挑战提供了强大的解决方案.
- 该方法显示了各种增量学习场景的巨大潜力,提高了模型的适应性和知识保留.
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