非平稳数据流下的持续学习灾难性遗忘问题求解策略综述

Review of catastrophic forgetting problems solving strategies forcontinual learning under non-stationary data streams

持续学习作为一种在非平稳数据流中不断学习新任务并能保持旧任务性能的特殊机器学习范例,是视觉计算、自主机器人等领域的研究热点,但现阶段灾难性遗忘问题仍然是持续学习的一个巨大挑战。围绕持续学习灾难性遗忘问题展开综述研究,分析了灾难性遗忘问题缓解机理,并从模型参数、训练数据和网络架构三个层面探讨了灾难性遗忘问题求解策略,包括正则化策略、重放策略、动态架构策略和联合策略;根据现有文献凝练了灾难性遗忘方法的评估指标,并对比了不同灾难性遗忘问题的求解策略性能。最后对持续学习相关研究指出了未来的研究方向,以期为研究持续学习灾难性遗忘问题提供借鉴和参考。

Continual learning,as a special machine learning paradigm that continuously learns new tasks in non-stationary data streams and can maintain the performance of old tasks,is a hot research topic in fields such as visual computing and autonomous robotics,but at this stage,the catastrophic forgetting problem is still a great challenge for continuous learning.This paper conducted a review study on the catastrophic forgetting problem of continual learning,analyzed the mechanism of catastrophic forgetting problem mitigation and explored the catastrophic forgetting problem solving strategies at three levels,included regularization strategy,replay strategy,dynamic architecture strategy and joint strategy,in terms of model parameters,training data and network architecture.According to the existing literature,this paper condensed the evaluation index of the catastrophic forgetting method and compared the performance of solving strategies for different catastrophic forgetting problems.Finally,it pointed out the future research direction of continual learning,to provide references for the study of continuous learning catastrophic forgetting problems.