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一种基于随机权神经网络的类增量学习与记忆融合方法

李德鹏 曾志刚

李德鹏, 曾志刚. 一种基于随机权神经网络的类增量学习与记忆融合方法. 自动化学报, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c220312
引用本文: 李德鹏, 曾志刚. 一种基于随机权神经网络的类增量学习与记忆融合方法. 自动化学报, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c220312
Li De-Peng, Zeng Zhi-Gang. A class incremental learning and memory fusion method using random weight neural networks. Acta Automatica Sinica, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c220312
Citation: Li De-Peng, Zeng Zhi-Gang. A class incremental learning and memory fusion method using random weight neural networks. Acta Automatica Sinica, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c220312

一种基于随机权神经网络的类增量学习与记忆融合方法

doi: 10.16383/j.aas.c220312
基金项目: 科技部科技创新2030重大项目(2021ZD0201300), 中央高校基本科研业务费专项资金(YCJJ202203012), 国家自然科学基金(U1913602, 61936004), 111计算智能与智能控制项目(B18024) 资助
详细信息
    作者简介:

    李德鹏:华中科技大学人工智能与自动化学院博士研究生. 主要研究方向为增量学习, 对抗机器学习, 脑启发神经网络, 计算机视觉. E-mail: dpli@hust.edu.cn

    曾志刚:华中科技大学人工智能与自动化学院教授. 主要研究方向为神经网络理论与应用, 动力系统稳定性, 联想记忆. 本文通信作者. E-mail: zgzeng@huat.edu.cn

A Class Incremental Learning and Memory Fusion Method Using Random Weight Neural Networks

Funds: Supported by National Key R&D Program of China (2021ZD0201300), the Fundamental Research Funds for the Central Universities (YCJJ202203012), National Natural Science Foundation of China (U1913602, 61936004), 111 Project on Computational Intelligence and Intelligent Control (B18024)
More Information
    Author Bio:

    LI De-Peng Ph. D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers incremental learning, adversarial machine learning, brain-inspired neural networks, and computer vision

    ZENG Zhi-Gang Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers neural networks, stability analysis of dynamic systems, associative memories. Corresponding author of this paper

  • 摘要: 连续学习多个任务的能力对于通用人工智能的发展至关重要. 现有人工神经网络在单一任务上具有出色表现, 但在开放环境中依次面对不同任务时非常容易发生灾难性遗忘现象, 即联结主义模型在学习新任务时会迅速地忘记旧任务. 为了解决这个问题, 本文将随机权神经网络与生物大脑的相关工作机制联系起来, 提出了一种新的再可塑性启发的随机化网络(Metaplasticity-inspired randomized network, MRNet)用于类增量学习场景, 使得单一模型在不访问旧任务数据的情况下能够从未知的任务序列中学习与记忆融合. 首先, 以前馈方式构造了具有解析解的通用连续学习框架, 用于有效兼容新任务中出现的新类别; 然后, 基于突触可塑性设计了具备记忆功能的权值重要性矩阵, 自适应地调整网络参数以避免发生遗忘; 最后, 所提方法的有效性和高效性通过5个评价指标, 5个基准任务序列和10个比较方法在类增量学习场景中得到验证.
  • 图  1  三种连续学习场景

    Fig.  1  Three continual learning scenarios

    图  2  用于连续学习的MRNet结构

    Fig.  2  MRNet architecture for CL

    图  3  FashionMNIST-10/5任务序列

    Fig.  3  FashionMNIST-10/5 task sequence

    图  4  CIFAR-100任务序列

    Fig.  4  CIFAR-100 task sequence

    图  5  不同方法在CIFAR-100任务序列上的分类精度曲线

    Fig.  5  Classification accuracy curve of different methods on CIFAR-100 task sequence (a) Five twenty-class classification tasks (b) Ten ten-class classification tasks

    表  1  不同类增量学习方法的特性

    Table  1  Characteristics of different Class-IL methods

    方法无需多次访问无需逐层优化无需数据存储无需网络扩展
    重放×××
    扩展×××
    正则化××
    MRNet
    下载: 导出CSV

    表  2  连续学习FashionMNIST-10/5任务序列对比实验

    Table  2  Comparative experiments on continuously learning FashionMNIST-10/5 task sequence

    方法指标
    ACCBWTFWTTimeNo. Para.
    BLS19.93±0.228.17±0.240.25
    L226.55±6.2759.12±2.731.28
    JT~96.61
    EWC34.96±7.62−0.7248±0.0953−0.0544±0.030069.21±4.1011.48
    MAS38.54±3.49−0.4781±0.0561−0.2576±0.0548110.26±1.743.83
    SI56.19±3.21−0.3803±0.0631−0.1329±0.050467.67±2.255.11
    OWM79.16±1.11−0.1844±0.0197−0.0635±0.007840.38±7.093.18
    GEM81.98±2.80−0.0586±0.0654−0.1093±0.051045.73±1.171.28
    PCL82.13±0.61−0.1385±0.0413−0.0647±0.0172348.75±9.831.28
    IL2M84.61±2.95−0.0712±0.02730.0258±0.024844.18±1.341.28
    MRNet93.07±0.740.0458±0.0069−0.0261±0.003511.38±0.290.83
    下载: 导出CSV

    表  3  连续学习ImageNet-200任务序列对比实验

    Table  3  Comparative experiments on continuously learning ImageNet-200 task sequence

    方法任务序列
    ImageNet-200/10ImageNet-200/50
    IL2M54.13±11.3047.84±18.85
    OWM55.93±14.2949.67±20.98
    PCL56.41±9.7552.46±8.95
    MRNet56.50±9.1355.93±11.51
    下载: 导出CSV

    表  4  权衡系数灵敏度分析

    Table  4  Sensitivity analysis on the trade-off coefficients

    保护程度评价指标
    $\text{A}_1$$\text{A}_2$$\text{A}_3$$\text{A}_4$$\text{A}_5$BWTFWT
    184.4542.8828.2020.5117.45−0.84200.0001
    $10^2$84.4575.4868.5761.5455.65−0.3629−0.0015
    $10^4$84.4582.3380.9078.4677.86−0.0615−0.0253
    $10^6$84.4571.4861.3749.8141.11−0.0199−0.5263
    $10^8$84.4544.3531.0523.2918.620.0003−0.8270
    下载: 导出CSV

    表  5  MRNet结构分析

    Table  5  Analysis on MRNet architecture

    有无直连评价指标
    $\text{A}_1$$\text{A}_2$$\text{A}_3$$\text{A}_4$$\text{A}_5$BWTFWT
    ×98.2092.5893.9893.3492.61−0.0199−0.0560
    99.8734.1433.8332.0128.40−0.1304−0.1883
    下载: 导出CSV
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