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类别增量学习研究进展和性能评价

朱飞 张煦尧 刘成林

朱飞, 张煦尧, 刘成林. 类别增量学习研究进展和性能评价. 自动化学报, 2023, 49(3): 635−660 doi: 10.16383/j.aas.c220588
引用本文: 朱飞, 张煦尧, 刘成林. 类别增量学习研究进展和性能评价. 自动化学报, 2023, 49(3): 635−660 doi: 10.16383/j.aas.c220588
Zhu Fei, Zhang Xu-Yao, Liu Cheng-Lin. Class incremental learning: A review and performance evaluation. Acta Automatica Sinica, 2023, 49(3): 635−660 doi: 10.16383/j.aas.c220588
Citation: Zhu Fei, Zhang Xu-Yao, Liu Cheng-Lin. Class incremental learning: A review and performance evaluation. Acta Automatica Sinica, 2023, 49(3): 635−660 doi: 10.16383/j.aas.c220588

类别增量学习研究进展和性能评价

doi: 10.16383/j.aas.c220588
基金项目: 创新2030“新一代人工智能重大项目” (2018AAA0100400), 国家自然科学基金 (61836014, 62222609, 62076236, 61721004), 中国科学院前沿科学重点研究项目 (ZDBS-LY-7004), 中国科学院青年创新促进会项目 (2019141)资助
详细信息
    作者简介:

    朱飞:中国科学院自动化研究所博士研究生. 2018年获得清华大学学士学位. 主要研究方向为模式识别和机器学习. E-mail: zhufei2018@ia.ac.cn

    张煦尧:中国科学院自动化研究所副研究员. 2008年获得武汉大学学士学位, 2013年获中国科学院大学博士学位. 主要研究方向为模式识别, 机器学习和文字识别. E-mail: xyz@nlpr.ia.ac.cn

    刘成林:中国科学院自动化研究所研究员. 主要研究方向为图像处理, 模式识别, 机器学习, 文档分析, 文字识别. 本文通信作者. E-mail: liucl@nlpr.ia.ac.cn

Class Incremental Learning: A Review and Performance Evaluation

Funds: Supported by the National Key Research and Development Program (2018AAA0100400), National Natural Science Foundation of China (61836014, 62222609, 62076236, 61721004), Key Research Program of Frontier Sciences of Chinese Academy of Sciences (ZDBS-LY-7004), and Youth Innovation Promotion Association of Chinese Academy of Sciences (2019141)
More Information
    Author Bio:

    ZHU Fei Ph.D. candidate at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from Tsinghua in 2018. His research interest covers pattern recognition and machine learning

    ZHANG Xu-Yao Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from Wuhan University in 2008 and Ph.D. degree from the University of Chinese Academy of Sciences in 2013. His research interest covers pattern recognition, machine learning, and handwriting recognition

    LIU Cheng-Lin Professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers image processing, pattern recognition, machine learning, and especially the applications to document analysis and recognition. Corresponding author of this paper

  • 摘要: 机器学习技术成功地应用于计算机视觉、自然语言处理和语音识别等众多领域. 然而, 现有的大多数机器学习模型在部署后类别和参数是固定的, 只能泛化到训练集中出现的类别, 无法增量式地学习新类别. 在实际应用中, 新的类别或任务会源源不断地出现, 这要求模型能够像人类一样在较好地保持已有类别知识的基础上持续地学习新类别知识. 近年来新兴的类别增量学习研究方向, 旨在使得模型能够在开放、动态的环境中持续学习新类别的同时保持对旧类别的判别能力(防止“灾难性遗忘”). 本文对类别增量学习(Class-incremental learning, CIL)方法进行了详细综述. 根据克服遗忘的技术思路, 将现有方法分为基于参数正则化、基于知识蒸馏、基于数据回放、基于特征回放和基于网络结构的五类方法, 对每类方法的优缺点进行了总结. 此外, 本文在常用数据集上对代表性方法进行了实验评估, 并通过实验结果对现有算法的性能进行了比较分析. 最后, 对类别增量学习的研究趋势进行展望.
  • 图  1  真实开放环境中机器学习系统的工作流程

    Fig.  1  Illustrations of the life cycle of a machine learning system in the open-world applications

    图  2  任务和类别增量学习示意图(本文关注类别增量学习)

    Fig.  2  Illustrations of task and class incremental learning (We focus on class incremental learning)

    图  3  类别增量学习方法分类图

    Fig.  3  The classification of class incremental learning methods

    图  4  OWM[46]方法原理示意图

    Fig.  4  Schematic diagram of OWM[46]

    图  5  类别增量学习中的知识蒸馏策略

    Fig.  5  Knowledge distillation strategies in class incremental learning

    图  6  LwM[51]中的注意力损失能够有效减少模型遗忘

    Fig.  6  Attention distillation loss in LwM[51] alleviates attention regions forgetting

    图  7  特征蒸馏减少特征分布漂移

    Fig.  7  Feature distillation loss alleviates feature distribution deviation

    图  8  PODnet[53]方法示意图

    Fig.  8  Illustration of PODnet[53]

    图  9  增量学习中样本关系知识蒸馏的不同策略

    Fig.  9  Illustration of relation knowledge distillation strategies in class incremental learning

    图  10  基于数据回放的类别增量学习方法主要包括 (a) 真实数据回放; (b) 生成数据回放

    Fig.  10  Data replay based class incremental learning methods include (a) real data replay and; (b) generative data replay

    图  11  启发式旧类别采样策略示意图

    Fig.  11  Illustration of heuristic sampling strategies

    图  12  三种样本保存策略T-SNE[72]可视化效果对比图

    Fig.  12  The T-SNE[72] results of three exemplar methods

    图  13  基于梯度匹配算法的数据集提炼方法示意图

    Fig.  13  Illustration of gradient matching algorithm for dataset condensation

    图  14  UCIR[30]方法示意图

    Fig.  14  Illustration of UCIR[30] for class incremental learning

    图  15  BiC[79]方法偏差校准示意图

    Fig.  15  Illustration of bias correction in BiC[79]

    图  16  生成样本可视化. 左侧为条件对抗生成网络生成的旧类别样本[99], 右侧为判别模型生成的旧类别样本[95]

    Fig.  16  Visualization of generated samples. Examples on left part are generated via conditional GAN, while the right ones are generated via deep inversion[95]

    图  17  特征适应网络[103]示意图. 特征适应网络将保存的旧类别特征向量投影到新的特征空间, 来联合训练分类器

    Fig.  17  Illustration of feature adaptation network (FAN)[103]. FAN transforms the preserved feature vectors of old classes into the new feature space

    图  18  PASS方法示意图

    Fig.  18  Illustration of PASS

    图  19  语义增强策略为旧类别隐式地生成无限多伪特征实例[106]

    Fig.  19  Semantic augmentation generates infinite deep features for old classes implicitly[106]

    图  20  两种特征生成方法示意图

    Fig.  20  Illustration of two types feature generation strategies

    图  21  动态融合网络示意图[121]

    Fig.  21  Illustration of adaptive aggregation networks[121]

    图  22  DER[125]方法示意图

    Fig.  22  Illustration of DER[125]

    图  23  动态结构重组方法SSRE[114]示意图

    Fig.  23  Illustration of dynamic structure reorganization method SSRE[114]

    图  24  代表性类别增量学习方法在CIFAR-100和ImageNet-Sub数据集上的性能比较. 数据回放方法为每个旧类别保存10个样本. 从左到右依次为5, 10和25阶段增量学习设定

    Fig.  24  Comparisons of the step-wise incremental accuracies on CIFAR-100 and ImageNet-Sub under three different settings: 5, 10, 25 incremental phases. 10 samples are saved for each old class in data replay based methods

    表  1  不同增量学习设定对比

    Table  1  Comparison of incremental learning settings

    设定说明
    数据增量类别集不变, 数据以在线的形式到来, 即传统的在线学习
    任务增量类别集变化, 推理阶段在各自任务内部分类
    类别增量类别集变化, 推理阶段在所有已学习类别上分类
    下载: 导出CSV

    表  2  类别增量学习评价指标

    Table  2  Evaluation metrics of class incremental learning

    增量准确率在所有已见类别上的分类准确率$A_t$
    增量遗忘率$F_{t}=\displaystyle\frac{1}{t-1}\sum_{i=1}^{t-1}f_{t}^{i}$
    平均增量准确率$\bar A = \displaystyle\frac{1}{T}\sum_{i=1}^{T}A_i$
    平均增量遗忘率$\bar F= \displaystyle\frac{1}{T}\sum_{i=1}^{T}F_i$
    下载: 导出CSV

    表  3  类别增量学习中的知识蒸馏方法总结

    Table  3  Summarization of knowledege distillation strategies in class incremental learning

    算法知识蒸馏损失知识蒸馏策略
    LwF, iCaRL, BiC式(2)惩罚输出概率分布变化
    EBIL, LwM式(3)惩罚重要特征变化
    UCIR式(4)惩罚最终特征变化
    PODnet式(5), 式(6)惩罚中间和最终特征变化
    TPCIL, MBP,
    Co2L
    式(7), 式(8),
    式(9)
    惩罚样本相似性关系变化
    DDE式(10), 式(11)惩罚邻域重构关系变化
    GeoDL式(12)惩罚连续子空间中特征变化
    DMC, GD式(2)无标记数据辅助知识蒸馏
    calibrateCIL式(2)合成数据辅助知识蒸馏
    下载: 导出CSV

    表  4  基于数据回放的类别增量学习中的新旧类别偏差校准方法总结

    Table  4  Summarization of bias calibration strategies in data replay based class incremental learning

    算法使用阶段平衡对象偏差校准策略
    E2E训练阶段训练数据两阶段的法, 构建平衡数据集微调模型
    GDumb训练阶段训练数据下采样法, 构建平衡数据集直接从头训练模型
    SS-IL训练阶段分类器解耦新旧类别的softmax操作和知识蒸馏
    RMM训练阶段训练数据平衡训练集, 通过强化学习算法管理新旧类别数据
    UCIR训练阶段分类器特征和分类权重模长归一化, 间隔排序损失
    iCaRL测试阶段分类器原型生成, 使用最近类别均值分类器
    BiC测试阶段分类器概率校准, 使用平衡验证集学习偏差校准变换
    WA测试阶段分类器对齐新旧类的权重向量的平均模长
    IL2M测试阶段分类器概率校准, 调节模型最终输出的概率分布
    下载: 导出CSV

    表  5  类别增量学习公用数据集的数量信息

    Table  5  Quantitative information of class incremental learning public data sets

    数据集数据数量类别数量平均类内样本
    MNIST60000106000
    CIFAR-1060000106000
    CIFAR-10060000100600
    CUB-2001178820058
    Tiny-ImageNet120000200600
    ImageNet-Sub60000100600
    ImageNet-Full128000010001280
    VGGFace2-Sub5417461000542
    GLandmarks-Sub3943671000394
    下载: 导出CSV

    表  6  基于样本回放的方法在CIFAR-100, ImageNet-Sub和ImageNet-Full上的平均增量准确率 (%) 比较

    Table  6  Comparisons of average incremental accuracies (%) on CIFAR-100, ImageNet-Sub, and ImageNet-Full

    存储个数算法发表出处CIFAR-100ImageNet-SubImageNet-Full
    $T=5$$T=10$$T=25$$T=5$$T=10$$T=25$$T=5$$T=10$$T=25$
    $R=10$iCaRL[29]$^{\dagger}$CVPR 201751.8044.7239.4959.6251.3740.3848.1742.5334.83
    BiC[79]$^{\dagger}$ECCV 201854.4649.8843.5361.7454.1739.37
    UCIR[30]$^{\dagger}$CVPR 201960.5857.5952.3371.8968.3557.6165.2160.4356.87
    UCIR+DDE[58]CVPR 202164.4162.0071.2069.0567.0464.98
    WA[31]$^{\dagger}$CVPR 202058.1146.9841.7861.1852.2340.5252.0547.57
    PODnet[53]$^{\dagger}$ECCV 202063.0960.7853.2376.6873.7059.0962.8863.7559.19
    PODnet+DDE[58]CVPR 202163.4060.5275.7673.0064.4162.09
    PASS+exemplar[13]$^{\dagger}$CVPR 202162.5464.96
    DMIL[134]CVPR 202267.0864.4175.7374.94
    $R=20$DMC[62]WACV 202038.2023.8043.0730.30
    GD[63]ICCV 201956.3951.3058.7057.70
    iCaRL[29]CVPR 201757.1252.6648.2265.4459.8852.9751.5046.8943.14
    iCaRL+Mnemonics[73]CVPR 202060.0057.3754.1372.3470.5067.1260.6158.6253.46
    iCaRL+AANets[121]CVPR 202164.2260.2656.4373.4571.7869.2263.9161.2856.97
    iCaRL+GeoDL[60]CVPR 202162.5461.4061.8470.1070.8670.7260.0257.9856.70
    BiC[79]CVPR 201959.3654.2050.0070.0764.9657.7362.6558.7253.47
    BiC+Mnemonics[73]CVPR 202060.6758.1155.5171.9270.7369.2264.6362.7160.20
    TPCIL[55]ECCV 202065.3463.5876.2774.8164.8962.88
    UCIR[30]CVPR 201963.1760.1457.5470.8468.3261.4464.4561.5756.56
    UCIR+DDE[58]CVPR 202165.2762.3672.3470.2067.5165.77
    UCIR+AANets[121]CVPR 202166.7465.2963.5072.5569.2267.6064.9462.3960.68
    UCIR+GeoDL[60]CVPR 202165.1465.0363.1273.8773.5571.7265.2364.4662.20
    UCIR+MRDC[71]ICLR 202273.5672.7070.5367.5365.29
    UCIR+CwD[142]CVPR 202267.2662.8956.8171.9469.3465.1057.4253.37
    WA[31]CVPR 202061.7056.3750.7871.2664.9953.6156.6952.3544.58
    PODnet[53]ECCV 202064.8363.1960.7275.5474.3368.3166.9564.1359.17
    PODnet+DDE[58]CVPR 202165.4264.1276.7175.4166.4264.71
    PODnet+AANets[121]CVPR 202166.3164.3162.3176.9675.5871.7867.7364.8561.78
    PODnet+MRDC[71]ICLR 202278.0876.0272.7268.9166.31
    PODnet+CwD[142]CVPR 202267.4464.6462.2476.9174.3467.4258.1856.01
    Mnemonics[73]CVPR 202063.3462.2860.9672.5871.3769.7464.5463.0161.00
    Mnemonics+AANets[121]CVPR 202167.5965.6663.3572.9171.9370.7065.2363.6061.53
    RMM[83]NeurIPS 202168.4267.1764.5673.5872.8372.3065.8164.1062.23
    DER[125]CVPR 202172.6072.4577.73
    SS-IL[78]ICCV 202163.0261.5258.02
    AFC[135]CVPR 202266.4964.9864.0676.8775.7573.3468.9067.02
    DMIL[134]CVPR 202268.0166.4777.2076.76
    下载: 导出CSV

    表  7  基于样本回放的方法在CIFAR-100, ImageNet-Sub和ImageNet-Full上的遗忘率 (%) 比较

    Table  7  Comparisons of average forgetting (%) on CIFAR-100, ImageNet-Sub, and ImageNet-Full

    存储个数算法发表出处CIFAR-100ImageNet-SubImageNet-Full
    $T=5$$T=10$$T=25$$T=5$$T=10$$T=25$$T=5$$T=10$$T=25$
    $R=20$iCaRL[29]CVPR 201731.8834.1036.4843.4045.8447.6026.0333.7638.80
    iCaRL+Mnemonics[73]CVPR 202025.9426.9228.9220.9624.1229.3220.2624.0417.49
    iCaRL+GeoDL[60]CVPR 202112.2021.1026.8426.8422.4424.8821.8422.8728.22
    BiC[79]CVPR 201931.4232.5034.6027.0431.0437.8825.0628.3433.17
    BiC+Mnemonics[73]CVPR 202022.4224.5025.5218.4319.2021.4318.3219.7220.50
    UCIR[30]CVPR 201918.7021.3426.4631.8833.4835.4024.0827.2930.30
    UCIR+GeoDL[60]CVPR 20219.499.1012.0113.7812.6815.2111.0312.8115.11
    WA[31]CVPR 202013.4917.0728.3224.4332.7241.0222.8828.1131.25
    Mnemonics[73]CVPR 202110.9113.3819.8017.4017.0820.8313.8515.8219.17
    下载: 导出CSV

    表  8  非样本回放类别增量学习方法平均增量准确率 (%) 比较

    Table  8  Comparisons of average incremental accuracies (%) of non-exemplar based class incremental learning methods

    算法发表出处CIFAR-100Tiny-ImageNetImageNet-Sub
    $T=5$$T=10$$T=20$$T=5$$T=10$$T=20$$T=10$
    LwF-MC[26]$^{\dagger}$ECCV 201633.3826.0119.7034.9121.3813.6835.79
    LwM[51]$^{\dagger}$CVPR 201939.6030.2420.5437.3220.4712.5532.57
    MUC[161]$^{\dagger}$ECCV 202049.2935.9928.9737.5026.2821.60
    calibrateCIL[64]$^{\dagger}$ICME 202160.8043.5838.0536.7227.6416.2841.11
    UCIR-DF[30]CVPR 201957.8248.69
    PODNet-DF[53]ECCV 202056.8552.61
    ABD[96]ICCV 202162.4058.9744.5541.64
    R-DFCIL[97]ECCV 202264.7861.7148.9147.60
    IL2A[106]$^{\dagger}$NeurIPS 202166.1658.2058.0147.2144.6940.0457.98
    PASS[13]$^{\dagger}$CVPR 202163.8459.8758.0649.5347.1941.9962.09
    SSRE[114]CVPR 202265.8865.0461.7050.3948.9348.1767.69
    SDC-new[107, 115]CVPR 202066.2062.7059.2053.2950.4848.7968.60
    Fusion[115]CVPR 202266.9064.8061.5054.1652.6350.2469.30
    下载: 导出CSV

    表  9  类别增量学习方法对比与总结

    Table  9  Comparison and summary of class incremental learning methods

    方法分类包含子类代表文献核心思想优点缺点
    参数正则化参数重要性估计[25, 2728]显式约束重要参数更新, 或者约束梯度更新方向不需要保存样本, 模型更新快速, 时间、空间复杂度低分类器有严重偏差, 类别增量性能差
    子空间投影[4648]
    知识蒸馏重要特征蒸馏[26, 30, 5051, 53]保持新旧模型对给定数据的输出一致性能够较好地保持已有知识, 成为很多方法的基础组成部分需要保存上一增量阶段的模型, 占用存储空间
    样本关系蒸馏[5560]
    辅助数据蒸馏[6264, 134]
    数据回放真实数据回放[29, 66, 73]保存一小部分旧类别数据用于后续再学习类别增量学习性能好, 且易于实现容易过拟合存储的数据, 时间、空间复杂度高, 隐私性不好
    新旧偏差校准[3031, 7781]
    生成数据回放[95100]
    特征回放真实特征回放[103105]保存深度特征空间的旧类别特征来维持决策面性能较好, 时间、空间复杂度低随着增量过程中特征提取器的更新, 保存的旧类别特征有效性降低
    类别原型回放[13, 106107, 114115]
    生成特征回放[108109]
    网络结构结构动态扩展[121, 125]冻结已有网络参数, 新参数用于学习新类别较好地保持旧类别知识, 同时能够较充分地学习新类别网络参数量逐渐增大, 时间、空间复杂度高
    下载: 导出CSV
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  • 收稿日期:  2022-07-21
  • 录用日期:  2022-12-01
  • 网络出版日期:  2023-01-04
  • 刊出日期:  2023-03-20

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