Brain-inspired Continuous Learning: Technology, Application and Future
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摘要: 深度学习模型面对非独立同分布数据流时，新知识会覆盖旧知识，造成其性能大幅度下降。而持续学习(CL)技术可以从非独立同分布数据流中获取增量可用知识，不断积累新知识的同时无须从头学习，通过模仿类脑的学习与记忆机制达到类人智能。该文针对脑启发式持续学习方法进行综述。首先，回顾持续学习发展历程；其次，从类脑持续学习机制的角度，将持续学习研究方法分为经典方法与脑启发方法两类，对重放、正则化与稀疏化3种经典持续学习方法的研究现状进行总结，分析了其所面临的困境。为此，针对更接近类脑持续学习能力的突触、双系统、睡眠及模块化4类脑启发方法进行阐述分析与对比总结；最后，概述脑启发式持续学习的应用现状，并探讨了在现有技术条件下实现脑启发式持续学习所面临的挑战及其未来发展方向。Abstract: Deep learning model facing the non-independent and identically distributed data streams, the old knowledge will be covered by new knowledge, resulting in a significant performance degradation of model. Continuous Learning(CL) can acquire incremental available knowledge from non-independent and identically distributed data streams, continuously accumulate new knowledge without learning from scratch, and achieve human intelligence by imitating brain learning and memory mechanisms. In this paper, the brain-inspired continuous learning methods are reviewed. Firstly, the history of continuous learning is reviewed. Secondly, from the perspective of brain continuous learning mechanism, the research methods of continuous learning are divided into general methods and brain-inspired methods .The current research status of replay, regularization and sparsity, which are commonly used as the methods of continuous learning, are summarized, and their difficulties are analyzed under the existing technical conditions. To this end, four types of brain-inspired methods: synaptic, dual system, sleep and modularization, which are closer to the ability of brain continuous learning, are meticulously analyzed and compared . Finally, the application status of brain-inspire continuous learning are summarized, and the challenges and development of brain-inspire continuous learning under the existing technical conditions are discussed.
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表 1 脑启发式CL方法对比与总结
方法 文献 年份 仿生具体机制 核心思想 数据集 对比对象 实验结果 优势 不足 突触 文献[20] 2017 突触内部动力学 智能突触 MNIST EWC、随机梯度下降(Stochastic Gradient Descent, SGD) 平均分类准确率97.5%+,
EWC 97.3%+, SGD 75.6%+资源开销相较于
其他3类脑启发
方法较小类增量场景中相较于
其他3类脑启发
方法表现较差文献[21] 2017 突触可塑性 选择性调整权重可塑性 MNIST 梯度下降(Gradient Descent, GD) 网络容量1000个突触内时，EWC优于GD 文献[60] 2018 突触可塑性 以无监督和在线方式计算参数重要性 MIT Scenes, Caltech- Aircraft[79], SVHN[80] LwF、文献[81]、文
献[82]、 EWC、SI连续实验中，2任务平均遗忘率最低,多任务平均遗忘率为0.49%，平均准确率比SI高2% 海马体
新皮
质层文献[65] 2014 CLS 双网络记忆模型 – 传统双网络记忆模型 召回结果：所提模型Goodness为0.942，
传统模型为0.718对增量任务流适应力强于
突触方法，内存开销小于
模块化方法，知识重用率
高于突触与睡眠方法资源开销高于突触
与睡眠方法文献[22] 2017 海马体记忆重放 深度生成重放 MNIST, SVHN LwF 平均准确率：MNIST(Old)→SVHN(New): 95%+; SVHN (Old)→MNIST (New): 88%+ 文献[33] 2018 CLS 双记忆自组织架构 CORe50 VGG + FT 平均准确率:实例79.43%,
类别93.92%文献[66] 2020 海马体经验重放 使用自动编码器顺序编码网络权值集 MNIST EWC 50个分离的MNIST数据集上平均准确率为95.7%, EWC为70%+ 文献[37] 2020 神经元活动模式 生成重放GR MNIST LwF, EWC, SI 类增量MNIST中, GR: 95%+, LwF, EWC, SI: 40%+ 睡眠 文献[72] 2018 海马体-新皮质层、睡眠重放 生成重放 CIFAR-100 iCaRL 平均准确率，IFAR-100:
rNet:75%+, iCaRL: 0%+清理内存和调整突触
可塑性机制可与其他
脑启发方法结合缓解灾难性遗忘效果
相较于其他3类
脑启发方法较差文献[24] 2019 睡眠巩固记忆 SNN与ANN切换 MNIST 全卷积网络(Full Convolutional
Network, FCN)平均准确率：FCN: 18%+,
Sleep: 40%文献[74] 2019 睡眠与做梦机制 清醒与睡眠状态
切换– – 存储容量从α=0.14扩展到最大值α=1 模块化 文献[29] 2015 神经调节 模块化网络 – – 神经模块化性能94% 缓解灾难性遗忘效果较
佳，知识重用率相较于
突触、睡眠方法较高内存开销高于其他3类脑启发方法 文献[78] 2017 神经调节 扩散的神经调节 – – 验证文献[29]所提的局部与特定任务的学习可以形成功能模块，缓解灾难性遗忘 文献[25] 2018 刺激分离 阻塞训练 2维叶*分枝空间的大型数据集 – 200个试验序列中，平均精度为90%+ 
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表 2 CL方法总结对比
类型 方法 核心思想 抗遗忘 模型规模控制 内存开销 计算开销 鲁棒性 泛化性 经典方法 重放 重放相关数据以恢复记忆 Ⅲ Ⅱ Ⅰ Ⅰ Ⅱ Ⅱ 正则化 保护重要参数 Ⅰ Ⅱ Ⅱ Ⅱ Ⅰ Ⅱ 稀疏化 隔离参数 Ⅲ Ⅰ Ⅰ Ⅰ Ⅲ Ⅲ 脑启发方法 双记忆系统 长短期记忆网络配合 Ⅲ Ⅱ Ⅰ Ⅰ Ⅲ Ⅱ 突触 在线方式计算突触重要性 Ⅱ Ⅲ Ⅱ Ⅱ Ⅲ Ⅲ 模块化 隔离参数 Ⅲ Ⅱ Ⅰ Ⅰ Ⅲ Ⅲ 睡眠 睡眠与清醒状态间切换 Ⅰ Ⅲ Ⅲ Ⅲ Ⅱ Ⅰ
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表 3 本文出现的中英文对照表
中文 英文 简称 持续学习 Continuous Learning CL 人工神经网络 Artificial Neural Network ANN 不遗忘学习 Learning without Forgetting LwF 弹性权值巩固 Elastic Weight Consolidation EWC 深度生成重放 Deep Generative Replay DGR 增长双记忆模型 Growing Dual-Memory GDM 生成重放 Generative Replay GR 增益扰动网络 Beneficial Perturbation Network BPN 智能突触 Synaptic Intelligence SI 记忆感知突触 Memory Aware Synapses MAS 互补学习系统 Complementary Learning Systems CLS 脉冲神经网络 Spiking Neural Network SNN 随机梯度下降 Stochastic Gradient Descent SGD 梯度下降 Gradient Descent GD 全卷积网络 Full Convolutional Network FCN 手写数字数据集 Mixed National Institute of Standards
and Technology databaseMNIST
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