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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.Abstract: Machine learning has been successfully applied in many fields such as computer vision, natural language processing, and speech recognition. However, in the current machine learning systems, models are often fixed after training. Consequently, they can only generalize to classes that appear in the training set, and cannot learn newly emerged classes continuously. In real-world applications, new classes or tasks will appear continuously, which requires the model to continuously learn new knowledge without forgetting the knowledge of previous seen classes. The emerging research direction of class incremental learning aims to enable models to continuously learn new classes while preserving the discrimination ability of old classes (defying “catastrophic forgetting”) in the open and dynamic environment. This paper provides a comprehensive overview of class incremental learning (CIL) developed in recent years. Specifically, existing methods are grouped into five categories: parameter regularization based, knowledge distillation based, data replay based, feature replay based and network structure based methods. The advantages and disadvantages of each method are summarized. In addition, extensive experiments are conducted to evaluate and compare those representative methods on benchmark datasets. Finally, this paper prospects the future research directions of class incremental learning.
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Key words:
- Incremental learning /
- continual learning /
- catastrophic forgetting /
- machine learning /
- deep learning
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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 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 7 特征蒸馏减少特征分布漂移
Fig. 7 Feature distillation loss alleviates feature distribution deviation
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 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 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 
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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 $\bar F= \displaystyle\frac{1}{T}\sum_{i=1}^{T}F_i$
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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
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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
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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 MNIST 60000 10 6000 CIFAR-10 60000 10 6000 CIFAR-100 60000 100 600 CUB-200 11788 200 58 Tiny-ImageNet 120000 200 600 ImageNet-Sub 60000 100 600 ImageNet-Full 1280000 1000 1280 VGGFace2-Sub 541746 1000 542 GLandmarks-Sub 394367 1000 394
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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-100 ImageNet-Sub ImageNet-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 2017 51.80 44.72 39.49 59.62 51.37 40.38 48.17 42.53 34.83 BiC[79]$^{\dagger}$ ECCV 2018 54.46 49.88 43.53 61.74 54.17 39.37 UCIR[30]$^{\dagger}$ CVPR 2019 60.58 57.59 52.33 71.89 68.35 57.61 65.21 60.43 56.87 UCIR+DDE[58] CVPR 2021 64.41 62.00 — 71.20 69.05 — 67.04 64.98 — WA[31]$^{\dagger}$ CVPR 2020 58.11 46.98 41.78 61.18 52.23 40.52 52.05 47.57 PODnet[53]$^{\dagger}$ ECCV 2020 63.09 60.78 53.23 76.68 73.70 59.09 62.88 63.75 59.19 PODnet+DDE[58] CVPR 2021 63.40 60.52 — 75.76 73.00 — 64.41 62.09 — PASS+exemplar[13]$^{\dagger}$ CVPR 2021 62.54 64.96 — — — — — — — DMIL[134] CVPR 2022 67.08 64.41 — 75.73 74.94 — — — — $R=20$ DMC[62] WACV 2020 38.20 23.80 — 43.07 30.30 — — — — GD[63] ICCV 2019 56.39 51.30 — 58.70 57.70 — — — — iCaRL[29] CVPR 2017 57.12 52.66 48.22 65.44 59.88 52.97 51.50 46.89 43.14 iCaRL+Mnemonics[73] CVPR 2020 60.00 57.37 54.13 72.34 70.50 67.12 60.61 58.62 53.46 iCaRL+AANets[121] CVPR 2021 64.22 60.26 56.43 73.45 71.78 69.22 63.91 61.28 56.97 iCaRL+GeoDL[60] CVPR 2021 62.54 61.40 61.84 70.10 70.86 70.72 60.02 57.98 56.70 BiC[79] CVPR 2019 59.36 54.20 50.00 70.07 64.96 57.73 62.65 58.72 53.47 BiC+Mnemonics[73] CVPR 2020 60.67 58.11 55.51 71.92 70.73 69.22 64.63 62.71 60.20 TPCIL[55] ECCV 2020 65.34 63.58 — 76.27 74.81 — 64.89 62.88 — UCIR[30] CVPR 2019 63.17 60.14 57.54 70.84 68.32 61.44 64.45 61.57 56.56 UCIR+DDE[58] CVPR 2021 65.27 62.36 — 72.34 70.20 — 67.51 65.77 — UCIR+AANets[121] CVPR 2021 66.74 65.29 63.50 72.55 69.22 67.60 64.94 62.39 60.68 UCIR+GeoDL[60] CVPR 2021 65.14 65.03 63.12 73.87 73.55 71.72 65.23 64.46 62.20 UCIR+MRDC[71] ICLR 2022 — — — 73.56 72.70 70.53 67.53 65.29 — UCIR+CwD[142] CVPR 2022 67.26 62.89 56.81 71.94 69.34 65.10 57.42 53.37 — WA[31] CVPR 2020 61.70 56.37 50.78 71.26 64.99 53.61 56.69 52.35 44.58 PODnet[53] ECCV 2020 64.83 63.19 60.72 75.54 74.33 68.31 66.95 64.13 59.17 PODnet+DDE[58] CVPR 2021 65.42 64.12 — 76.71 75.41 — 66.42 64.71 — PODnet+AANets[121] CVPR 2021 66.31 64.31 62.31 76.96 75.58 71.78 67.73 64.85 61.78 PODnet+MRDC[71] ICLR 2022 — — — 78.08 76.02 72.72 68.91 66.31 — PODnet+CwD[142] CVPR 2022 67.44 64.64 62.24 76.91 74.34 67.42 58.18 56.01 — Mnemonics[73] CVPR 2020 63.34 62.28 60.96 72.58 71.37 69.74 64.54 63.01 61.00 Mnemonics+AANets[121] CVPR 2021 67.59 65.66 63.35 72.91 71.93 70.70 65.23 63.60 61.53 RMM[83] NeurIPS 2021 68.42 67.17 64.56 73.58 72.83 72.30 65.81 64.10 62.23 DER[125] CVPR 2021 72.60 72.45 — — 77.73 — — — — SS-IL[78] ICCV 2021 63.02 61.52 58.02 — — — — — — AFC[135] CVPR 2022 66.49 64.98 64.06 76.87 75.75 73.34 68.90 67.02 — DMIL[134] CVPR 2022 68.01 66.47 — 77.20 76.76 — — — —
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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-100 ImageNet-Sub ImageNet-Full $T=5$ $T=10$ $T=25$ $T=5$ $T=10$ $T=25$ $T=5$ $T=10$ $T=25$ $R=20$ iCaRL[29] CVPR 2017 31.88 34.10 36.48 43.40 45.84 47.60 26.03 33.76 38.80 iCaRL+Mnemonics[73] CVPR 2020 25.94 26.92 28.92 20.96 24.12 29.32 20.26 24.04 17.49 iCaRL+GeoDL[60] CVPR 2021 12.20 21.10 26.84 26.84 22.44 24.88 21.84 22.87 28.22 BiC[79] CVPR 2019 31.42 32.50 34.60 27.04 31.04 37.88 25.06 28.34 33.17 BiC+Mnemonics[73] CVPR 2020 22.42 24.50 25.52 18.43 19.20 21.43 18.32 19.72 20.50 UCIR[30] CVPR 2019 18.70 21.34 26.46 31.88 33.48 35.40 24.08 27.29 30.30 UCIR+GeoDL[60] CVPR 2021 9.49 9.10 12.01 13.78 12.68 15.21 11.03 12.81 15.11 WA[31] CVPR 2020 13.49 17.07 28.32 24.43 32.72 41.02 22.88 28.11 31.25 Mnemonics[73] CVPR 2021 10.91 13.38 19.80 17.40 17.08 20.83 13.85 15.82 19.17
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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-100 Tiny-ImageNet ImageNet-Sub $T=5$ $T=10$ $T=20$ $T=5$ $T=10$ $T=20$ $T=10$ LwF-MC[26]$^{\dagger}$ ECCV 2016 33.38 26.01 19.70 34.91 21.38 13.68 35.79 LwM[51]$^{\dagger}$ CVPR 2019 39.60 30.24 20.54 37.32 20.47 12.55 32.57 MUC[161]$^{\dagger}$ ECCV 2020 49.29 35.99 28.97 37.50 26.28 21.60 — calibrateCIL[64]$^{\dagger}$ ICME 2021 60.80 43.58 38.05 36.72 27.64 16.28 41.11 UCIR-DF[30] CVPR 2019 57.82 48.69 — — — — — PODNet-DF[53] ECCV 2020 56.85 52.61 — — — — — ABD[96] ICCV 2021 62.40 58.97 — 44.55 41.64 — — R-DFCIL[97] ECCV 2022 64.78 61.71 — 48.91 47.60 — — IL2A[106]$^{\dagger}$ NeurIPS 2021 66.16 58.20 58.01 47.21 44.69 40.04 57.98 PASS[13]$^{\dagger}$ CVPR 2021 63.84 59.87 58.06 49.53 47.19 41.99 62.09 SSRE[114] CVPR 2022 65.88 65.04 61.70 50.39 48.93 48.17 67.69 SDC-new[107, 115] CVPR 2020 66.20 62.70 59.20 53.29 50.48 48.79 68.60 Fusion[115] CVPR 2022 66.90 64.80 61.50 54.16 52.63 50.24 69.30
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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, 27−28] 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 [46−48] 知识蒸馏 重要特征蒸馏 [26, 30, 50−51, 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 [55−60] 辅助数据蒸馏 [62−64, 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 [30−31, 77−81] 生成数据回放 [95−100] 特征回放 真实特征回放 [103−105] 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, 106−107, 114−115] 生成特征回放 [108−109] 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
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