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深度模型的持续学习综述:理论、方法和应用

张东阳 陆子轩 刘军民 李澜宇

张东阳, 陆子轩, 刘军民, 李澜宇. 深度模型的持续学习综述:理论、方法和应用[J]. 电子与信息学报. doi: 10.11999/JEIT240095
引用本文: 张东阳, 陆子轩, 刘军民, 李澜宇. 深度模型的持续学习综述:理论、方法和应用[J]. 电子与信息学报. doi: 10.11999/JEIT240095
ZHANG Dongyang, LU Zixuan, LIU Junmin, LI Lanyu. A Survey of Continual Learning with Deep Networks: Theory, Method and Application[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240095
Citation: ZHANG Dongyang, LU Zixuan, LIU Junmin, LI Lanyu. A Survey of Continual Learning with Deep Networks: Theory, Method and Application[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240095

深度模型的持续学习综述:理论、方法和应用

doi: 10.11999/JEIT240095
基金项目: 国家自然科学基金(62276208, 12326607, 11991023),陕西省杰出青年科学基金(2024JC-JCQN-02)
详细信息
    作者简介:

    张东阳:男,博士生,研究方向为持续学习

    陆子轩:男,硕士生,研究方向为持续学习

    刘军民:男,教授,研究方向为多源图像融合与目标检测研究、深度学习的泛化性与可解释性研究

    李澜宇:男,博士,研究方向为智能遥感

    通讯作者:

    刘军民 junminliu@mail.xjtu.edu.cn

  • 中图分类号: TP181; TP183

A Survey of Continual Learning with Deep Networks: Theory, Method and Application

Funds: The National Natural Science Foundation of China (62276208, 12326607, 11991023), The Natural Science Basic Research Program of Shaanxi Province (2024JC-JCQN-02)
  • 摘要: 自然界中的生物需要在其一生中不断地学习并适应环境,这种持续学习的能力是生物学习系统的基础。尽管深度学习方法在计算机视觉和自然语言处理领域取得了重要进展,但它们在连续学习任务时面临严重的灾难性遗忘问题,即模型在学习新知识时会遗忘旧知识,这在很大程度上限制了深度学习方法的应用。持续学习研究对人工智能系统的改进和应用具有重要意义。该文对深度模型的持续学习进行了全面回顾。首先介绍了持续学习的定义和典型设定,阐述了问题的关键。其次,将现有持续学习方法划分为基于正则化、基于回放、基于梯度和基于网络结构4类,分析了各类方法的优点和局限性。同时,该文强调并总结了持续学习领域的理论分析进展,建立了理论与方法之间的联系。此外,提供了常用的数据集和评价指标,以公正评判不同方法。最后,从多个领域的应用价值出发,讨论了深度持续方法面临的问题、挑战和未来研究方向。
  • 图  1  持续学习方法时间线路图

    图  2  不同正则化方法

    图  3  数据集蒸馏方法示意图

    图  4  持续学习过程中旧类别表征偏移

    图  5  基于梯度的方法示意图

    图  6  基于网络结构的方法

    表  1  持续学习的不同任务设定

    任务设定数据分布任务标识
    域增量学习$ p\left({\mathcal{X}}_{i}\right)\ne p\left({\mathcal{X}}_{j}\right),{\mathcal{Y}}_{i}={\mathcal{Y}}_{j}, \forall i\ne j $×
    任务增量学习$ p\left({\mathcal{X}}_{i}\right)\ne p\left({\mathcal{X}}_{j}\right),{\mathcal{Y}}_{i}\bigcap {\mathcal{Y}}_{j}=\varnothing , \forall i\ne j $
    类别增量学习$ p\left({\mathcal{X}}_{i}\right)\ne p\left({\mathcal{X}}_{j}\right),{\mathcal{Y}}_{i}\bigcap {\mathcal{Y}}_{j}=\varnothing , \forall i\ne j $×
    下载: 导出CSV

    表  2  持续学习与相关研究领域的区别

    研究领域 训练数据 测试数据 额外限制
    监督学习 $ {\mathcal{D}}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}} $ $ p\left({\mathcal{D}}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}n}\right)=p\left({\mathcal{D}}_{\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}\right) $
    多任务学习 $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t} $ $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t} $ $ p\left({\mathcal{D}}_{i}\right)\ne p\left({\mathcal{D}}_{j}\right), $ $ i\ne j $
    元学习 $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t-1} $ $ {\mathcal{D}}_{t} $ $ p\left({\mathcal{D}}_{i}\right)\ne p\left({\mathcal{D}}_{t}\right), $ $ i < t $
    迁移学习 $ {\mathcal{D}}_{\mathrm{s}\mathrm{r}\mathrm{c}},{\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $ $ p\left({\mathcal{D}}_{\mathrm{s}\mathrm{r}\mathrm{c}}\right)\ne p\left({\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}}\right) $
    域适应 $ {\mathcal{D}}_{\mathrm{s}\mathrm{r}\mathrm{c}},{\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $无标注信息
    域泛化 $ {\mathcal{D}}_{\mathrm{s}\mathrm{r}\mathrm{c}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $ $ {\mathcal{D}}_{\mathrm{t}\mathrm{g}\mathrm{t}} $无法访问
    持续学习 $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t} $ $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t} $ $ {\mathcal{D}}_{1},{\mathcal{D}}_{2},\cdots,{\mathcal{D}}_{t-1} $无法访问
    下载: 导出CSV

    表  3  持续学习方法分类及特点

    类别方法方法特点优缺点
    基于正则化参数正则化
    数据正则化
    任务偏向修正
    通过参数重要性估计对参数进行保护
    保持新旧模型对给定数据的输出一致性
    针对网络任务偏向问题提出不同的解决方案
    无需回放样本,但难以有效估计参数重要性,性能较差
    简单有效,但通常需要回放样本或特征以提高性能
    需要额外的修正训练,或额外的计算资源
    基于回放原始数据回放
    原始特征回放
    生成式回放
    回放部分任务的原始样本
    回放样本特征或类别原型
    使用生成模型进行数据回放
    简单有效,但回放样本占据空间较大
    节省存储空间,面临特征偏移问题
    生成数据的质量难以保证
    基于梯度梯度情景记忆
    子空间投影
    平坦极小点
    基于历史数据的梯度构建约束
    将参数梯度投影到子空间
    获取平坦极小点
    需要回放样本,并计算额外梯度
    能有效减缓遗忘,需要存储特征空间
    使用额外的技术手段,增加训练成本
    基于网络结构静态结构
    动态结构
    参数高效微调
    将网络参数分配给任务
    动态地扩张网络结构
    对预训练模型进行增量式微调
    模型容量有限,难以解决长序列任务的学习问题
    能有效减缓遗忘,但扩张网络带来额外存储和推理负担
    能有效减缓遗忘,但获取预训练模型需要成本
    下载: 导出CSV

    表  4  持续学习理论工作总结

    理论工作 主要结果 特点 对应方法
    概率模型 $ {\theta }_{t}\approx {\mathrm{argmax}}_{\theta }\mathrm{log}p\left({\mathcal{D}}_{t}|\theta \right)-{\left(\theta -{\theta }_{t-1}\right)}^{\mathrm{T}}{\boldsymbol{F}}_{1:t-1}(\theta -{\theta }_{t-1})/2 $ 通过对先前任务进行近似和估计,
    得到网络训练的正则损失
    参数正则化
    PAC学习 $ {\varepsilon }_{{\mathcal{D}}_{t}}\left(h\right)\le \dfrac{1}{t-1}{\displaystyle\sum }_{i=1}^{t-1}{\widehat{\varepsilon }}_{{D}_{i}}\left(h\right)+\dfrac{1}{2\left(t-1\right)}{\displaystyle\sum }_{i=1}^{t-1}{d}_{\mathcal{H}\Delta \mathcal{H}}\left({\mathcal{D}}_{i},{\mathcal{D}}_{t}\right)+{\lambda }_{t-1} $ 在PAC学习理论框架下,对网络的
    泛化误差进行界定
    数据正则化
    基于回放的方法
    神经正切核 $ {{\varDelta }}_{t-1}={\left\|\mathcal{K}\left({X}_{t-1},{X}_{t}\right){\left(\mathcal{K}\left({X}_{t},{X}_{t}\right)+\lambda I\right)}^{-1}{\stackrel{~}{y}}_{t}\right\|}_{2}^{2} $ 在神经正切核范式下,
    分析神经网络的遗忘问题
    基于梯度的方法
    任务分解 $ p\left(x\in {X}_{t,j}|\theta \right)=p\left(x\in {X}_{t,j}|{\theta }^{\left(t\right)}\right)p\left(x\in {X}_{t}|\theta \right) $ 将类别增量学习问题分解为任务
    内预测和任务标识预测两个子问题
    基于网络结构
    的方法
    下载: 导出CSV

    表  5  持续学习常用数据集

    数据集年份类别数数据量
    MNIST[212]19981060,000
    CIFAR-10[213]20091060,000
    CIFAR-100[213]200910060,000
    CUB-200[214]201120011,788
    Tiny-ImageNet[215]2015200120,000
    Sub-ImageNet[216]2009$ 100 $60,000
    Full-ImageNet[216]20091,0001,280,000
    5-datasets[217]202050260,000
    CORe50[218]20175015,000
    DomainNet[219]2019345590,000
    CCDB[220]20222--
    下载: 导出CSV
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  • 收稿日期:  2024-02-22
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