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深度对比学习综述

张重生 陈杰 李岐龙 邓斌权 王杰 陈承功

张重生, 陈杰, 李岐龙, 邓斌权, 王杰, 陈承功. 深度对比学习综述. 自动化学报, 2023, 49(1): 15−39 doi: 10.16383/j.aas.c220421
引用本文: 张重生, 陈杰, 李岐龙, 邓斌权, 王杰, 陈承功. 深度对比学习综述. 自动化学报, 2023, 49(1): 15−39 doi: 10.16383/j.aas.c220421
Zhang Chong-Sheng, Chen Jie, Li Qi-Long, Deng Bin-Quan, Wang Jie, Chen Cheng-Gong. Deep contrastive learning: A survey. Acta Automatica Sinica, 2023, 49(1): 15−39 doi: 10.16383/j.aas.c220421
Citation: Zhang Chong-Sheng, Chen Jie, Li Qi-Long, Deng Bin-Quan, Wang Jie, Chen Cheng-Gong. Deep contrastive learning: A survey. Acta Automatica Sinica, 2023, 49(1): 15−39 doi: 10.16383/j.aas.c220421

深度对比学习综述

doi: 10.16383/j.aas.c220421
基金项目: 科技部高端外国专家项目(G2021026016L)资助
详细信息
    作者简介:

    张重生:河南大学计算机与信息工程学院教授. 主要研究方向为长尾学习与不均衡学习, 基于深度学习的汉字识别和古文字计算. E-mail: cszhang@henu.edu.cn

    陈杰:河南大学计算机与信息工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别. E-mail: jiechen@henu.edu.cn

    李岐龙:河南大学计算机与信息工程学院博士研究生. 主要研究方向为对比学习和文字识别. 本文通信作者. E-mail: qilonghenu@henu.edu.cn

    邓斌权:河南大学计算机与信息工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别. E-mail: bqdeng@henu.edu.cn

    王杰:河南大学计算机与信息工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别. E-mail: wangjie@henu.edu.cn

    陈承功:河南大学计算机与信息工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别. E-mail: cgcheng@henu.edu.cn

Deep Contrastive Learning: A Survey

Funds: Supported by the High-end Foreign Expert Project of the Ministry of Science and Technology of China (G2021026016L)
More Information
    Author Bio:

    ZHANG Chong-Sheng Professor at the School of Computer and Information Engineering, Henan University. His research interest covers long-tail learning and imbalanced learning, deep learning based OCR and ancient character computing

    CHEN Jie Master student at the School of Computer and Information Engineering, Henan University. Her research interest covers computer vision and pattern recognition

    LI Qi-Long Ph.D. candidate at the School of Computer and Information Engineering, Henan University. His research interest covers contrastive learning and scene text recognition. Corresponding author of this paper

    DENG Bin-Quan Master student at the School of Computer and Information Engineering, Henan University. His research interest covers computer vision and pattern recognition

    WANG Jie Master student at the School of Computer and Information Engineering, Henan University. His research interest covers computer vision and pattern recognition

    CHEN Cheng-Gong Master student at the School of Computer and Information Engineering, Henan University. His research interest covers computer vision and pattern recognition

  • 摘要: 在深度学习中, 如何利用大量、易获取的无标注数据增强神经网络模型的特征表达能力, 是一个具有重要意义的研究问题, 而对比学习是解决该问题的有效方法之一, 近年来得到了学术界的广泛关注, 涌现出一大批新的研究方法和成果. 本文综合考察对比学习近年的发展和进步, 提出一种新的面向对比学习的归类方法, 该方法将现有对比学习方法归纳为5类, 包括: 1) 样本对构造; 2) 图像增广; 3) 网络架构; 4) 损失函数; 5) 应用. 基于提出的归类方法, 对现有对比研究成果进行系统综述, 并评述代表性方法的技术特点和区别, 系统对比分析现有对比学习方法在不同基准数据集上的性能表现. 本文还将梳理对比学习的学术发展史, 并探讨对比学习与自监督学习、度量学习的区别和联系. 最后, 本文将讨论对比学习的现存挑战, 并展望未来发展方向和趋势.
  • 图  1  对比学习方法归类

    Fig.  1  Taxonomy of contrastive learning methods

    图  2  常用的对比学习网络架构

    Fig.  2  Commonly used contrastive learning network architecture

    图  3  对比学习的整体流程及各模块的细分类方法

    Fig.  3  Overall framework of the contrastive learning process and the sub-category of each module

    图  4  困难负样本对示例

    Fig.  4  Example of hard negative pair

    图  5  假负样本示例

    Fig.  5  Example of false negative pair

    图  6  常用图像变换方法示例

    Fig.  6  Example of common image augmentations

    图  7  裁剪操作对正样本对构造的影响示例

    Fig.  7  The influence of constructing positive pairs by image crop

    图  8  同步对称网络架构

    Fig.  8  The architecture of synchronous symmetrical network

    图  9  同步非对称网络架构

    Fig.  9  The architecture of synchronous unsymmetrical network

    图  10  异步对称网络架构

    Fig.  10  The architecture of asynchronous symmetrical network

    图  11  BYOL网络架构

    Fig.  11  The architecture of BYOL

    图  12  SimSiam网络架构

    Fig.  12  The architecture of SimSiam

    图  13  HCSC网络架构

    Fig.  13  The architecture of HCSC

    图  14  不同类型的对比学习方法统计展示

    Fig.  14  The statistical results of different contrastive learning methods

    图  15  完全崩塌与维度崩塌示例

    Fig.  15  Example of complete collapse and dimensional collapse

    图  16  对比学习中一致性和均匀性的概念

    Fig.  16  The concept of uniformity and alignment in contrastive learning

    表  1  对比学习常用数据集总结

    Table  1  Summary of common datasets

    数据集任务类别个数图像总量
    ImageNet-1K[17]分类1 000128万
    Cifar10[18]分类106万
    Cifar100[18]分类1006万
    Food101[19]分类10110万
    Birdsnap[20]分类5005万
    Sun397[21]分类39711万
    Cars[22]分类1961.6万
    Aircraft[23]分类1001万
    DTD[24]分类475 640
    Pets[25]分类373 680
    Caltech-101[26]分类1019 144
    Flowers[27]分类1027 169
    VOC[28]检测&分割201万
    COCO[29]检测&分割8033万
    下载: 导出CSV

    表  2  本文所用符号总结

    Table  2  Summary of the symbols used in this paper

    符号说明
    $X$数据集合, 小写为其中的数据
    $Y$标签集合, 小写为其中的数据
    $T$图像增广方法
    $f$特征提取网络
    $g$投影头
    $s$相似度度量函数
    $h$特征向量, $h = f(x)$
    $z$投影向量, $z = g(h)$
    $c$聚类中心向量
    $\tau$温度系数
    下载: 导出CSV

    表  3  InfoNCE损失函数及其变种

    Table  3  InfoNCE loss and some varieties based on InfoNCE

    损失名文献年份会议/刊物公式主要改进
    InfoNCE[7]2019arXiv$-\ln \dfrac{{\exp (s(q,{h^ + }))}}{{\sum\nolimits_{{x_i} \in X} {\exp (s(q,{h_i}))} }}$初始的InfoNCE损失. 文献[3, 9, 3031, 38]等均采用InfoNCE损失函数.
    ProtoNCE[63]2021ICLR$ - \ln \dfrac{{\exp (s({z_i},{c_i})/\tau )}}{{\sum\limits_{j = 0}^r {\exp (s({z_i},{c_j})/\tau )} }}$从两个样本增广间的对比变为样本增广与聚类中心的对比. 注: ${c_i}$, ${c_j}$为聚类中心.
    总损失包括实例间的对比和实例−原型对比损失, 此处只列出实例-原型对比损失.
    DCL[71]2021ECCV$- \dfrac{ {s({z_i},z_i^ + )} }{\tau } + \ln \displaystyle\sum\limits_{j = 1}^{2N} { {1_{[j \ne i]} } } \exp (s({z_i},{z_j})/\tau)$去除负正耦合系数后通过化简得到该损失.
    DirectNCE[72]2022ICLR$-\ln \dfrac{\exp s((\widehat{h}'_i, \widehat{h}'^+_{i})/\tau)}{\sum\nolimits_j \exp s((\widehat{h}'_i, \widehat{h}'^+_{i})/\tau)}$ $\widehat{h}_i' = \widehat{h}_i'[0:d]$, 即取特征向量的前$d$个维度.
    FNCL[35]2022WACV$- \ln \dfrac{{\exp (s({z_i},z_i^ + )/\tau )}}{{\sum\limits_{j = 1}^{2N} {{1_{[j \ne i,j \ne {F_i}]}}\exp (s({z_i},{z_j})/\tau)}}}$${F_i}$为第$i$个样本的假负样本集.
    SCL[8]2020NIPS$- \displaystyle\sum\limits_{i \in I} {\dfrac{1}{ {p(i)} } } \sum\limits_{p \in p(i)} {\ln \dfrac{ {\exp (s({z_i},{z_p})/\tau )} }{ {\sum\nolimits_{a \in A(i)} {\exp (s({z_i},{z_a})/\tau )} } } }$将标签引入对比学习, $P(i)$是与第$i$个样本相同类的数据集合.
    下载: 导出CSV

    表  4  对比学习方法整体归类分析

    Table  4  Analysis of different contrastive learning methods based on our proposed taxonomy

    文献年份会议/刊物样本对构造图像增广网络架构损失函数数据标注
    [7]2019arXiv随机采样图像变换同步非对称InfoNCE类
    [2]2020ICML随机采样图像变换同步对称InfoNCE类
    [3]2020CVPR随机采样图像变换异步对称InfoNCE类
    [4]2020NIPS随机采样图像变换异步非对称传统损失
    [5]2020NIPS随机采样图像变换聚类/同步对称传统损失
    [8]2020NIPS随机采样图像变换同步对称InfoNCE类
    [9]2020NIPS随机采样图像变换同步对称混合损失部分
    [33]2020NIPS困难样本构造图像变换异步对称InfoNCE类
    [36]2020NIPS剔除假负样本图像变换同步对称InfoNCE类
    [37]2020arXiv正样本扩充图像变换异步对称InfoNCE类
    [42]2020NIPS正样本扩充图像变换同步对称InfoNCE类
    [45]2020ECCV构造多视图图像变换同步对称InfoNCE类
    [52]2020NIPS随机采样语义增广同步对称InfoNCE类
    [55]2020CVPR随机采样图像变换同步非对称InfoNCE类
    [57]2020NIPS随机采样图像变换同步非对称InfoNCE类
    [70]2020arXiv随机采样图像变换异步对称InfoNCE类
    [109]2020ECCV随机采样图像变换同步非对称InfoNCE类
    [6]2021CVPR随机采样图像变换异步非对称传统损失
    [32]2021ICCV困难样本构造图像变换异步对称InfoNCE类
    [34]2021CVPR困难样本构造图像变换同步对称混合损失部分
    [39]2021ICCV正样本扩充图像变换异步对称混合损失
    [40]2021CVPR正样本扩充图像变换同步对称InfoNCE类
    [46]2021CVPRW构造多视图图像变换同步对称混合损失
    [61]2021ICCV随机采样图像变换异步非对称InfoNCE类
    [63]2021ICLR随机采样图像变换聚类/异步对称InfoNCE类
    [64]2021CVPR随机采样图像变换聚类架构InfoNCE类
    [66]2021AAAI随机采样图像变换聚类/同步对称InfoNCE类
    [77]2021CVPR随机采样图像变换同步对称混合损失
    [79]2021ICCV随机采样图像变换同步对称混合损失部分
    [83]2021TGRS随机采样图像变换同步对称混合损失部分
    [78]2021TGRS随机采样图像变换同步对称InfoNCE类
    [85]2021CVPR随机采样图像变换同步对称InfoNCE类
    [35]2022WACV剔除假负样本图像变换同步对称InfoNCE类
    [41]2022CVPR正样本扩充图像变换同步非对称混合损失
    [43]2022ICLR正样本扩充图像变换异步对称混合损失部分
    [44]2022CVPR正样本扩充图像变换同步对称混合损失部分
    [47]2022CVPR随机采样图像变换任意架构InfoNCE类
    [48]2022CVPR随机采样图像合成异步对称InfoNCE类
    [54]2022TAI随机采样图像变换同步非对称InfoNCE类
    [65]2022CVPR剔除假负样本图像变换聚类/异步对称InfoNCE类
    [72]2022ICLR随机采样图像变换同步对称InfoNCE类
    [76]2022AAAI随机采样图像变换同步对称传统损失
    [94]2022ICLR随机采样图像变换同步对称InfoNCE类
    [98]2022CVPR随机采样图像变换同步对称InfoNCE类
    [103]2022ICLR随机采样图像变换同步对称混合损失
    下载: 导出CSV

    表  5  不同对比学习算法在ImageNet数据集上的分类效果

    Table  5  The classification results of different contrastive learning methods on ImageNet

    文献主干网络Top 1 (%)Top 5 (%)数据标注
    MoCov1[3]ResNet5060.6
    CPCv2[100]ResNet5063.885.3
    ResNet16171.590.1
    PCL[63]ResNet5067.6
    SimCLR[2]ResNet5069.389
    MoCov2[70]ResNet5071.1
    SimSiam[6]ResNet5071.3
    BT[101]ResNet5073.291
    VICReg[103]ResNet5073.291.1
    HCSC[65]ResNet5073.3
    MoCov3[61]ResNet5073.8
    Transformer76.5
    BYOL[4]ResNet5074.391.6
    SwAV[5]ResNet5075.3
    DINO[59]ResNet5075.3
    Transormer77
    TSC[96]ResNet5077.1
    SCL[8]ResNet5078.794.3
    PaCo[67]ResNet5079.3
    下载: 导出CSV

    表  6  不同对比学习算法在各数据集上的迁移学习效果

    Table  6  The transfer learning results of different contrastive learning methods on each dataset

    文献Food (%)Cifar10/Cifar100 (%)Birds (%)SUN (%)Cars (%)Aircraft (%)VOC (%)DTD (%)Pets (%)Caltech (%)Flowers (%)
    线性评估
    SimCLR[2]68.490.6/71.637.458.850.350.380.574.583.690.391.2
    SimCLRv2[9]73.992.4/7644.76154.951.181.276.58591.293.5
    BYOL[4]75.391.3/78.457.262.267.860.682.575.590.494.296.1
    微调评估
    MMCL[76]82.496.24/82.189.285.473.587.895.2
    SimCLR[2]88.297.7/85.975.963.591.388.184.173.289.292.197
    SimCLRv2[9]88.297.5/8674.964.691.887.684.174.789.992.397.2
    BYOL[4]88.597.8/86.176.363.791.688.185.476.291.793.897
    FNC[35]88.397.7/86.876.364.29288.584.77690.993.697.5
    SCL[8]87.297.42/84.375.25891.784.185.274.693.59196
    下载: 导出CSV

    表  7  不同半监督对比学习算法在ImageNet上的分类效果

    Table  7  The classification results of different semi-supervised contrastive learning methods on ImageNet

    文献Top 1 (%)Top 5 (%)
    1%10%1%10%
    PIRL[55]30.760.457.283.8
    PCL[63]75.385.6
    SimCLR[2]48.365.675.587.8
    BYOL[4]53.268.878.489
    SwAV[5]53.970.278.589.9
    BT[101]5569.779.289.3
    HCSC[65]55.568.780.988.6
    CoMatch[79]67.173.787.191.4
    SimCLRv2[9]73.977.591.593.4
    下载: 导出CSV

    表  8  不同对比学习算法在图像分割任务上的性能表现

    Table  8  The image segmentation results of different contrastive learning methods on VOC and COCO dataset

    文献APAPm
    VOC (%)COCO (%)COCO (%)
    BYOL[4]55.337.933.2
    SwAV[5]55.437.633.1
    SimCLR[2]55.537.933.3
    MoCov2[70]5739.234.3
    SimSiam[6]5739.234.4
    DenseCL[89]58.740.336.4
    下载: 导出CSV
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  • 收稿日期:  2022-05-22
  • 录用日期:  2022-08-22
  • 网络出版日期:  2022-12-23
  • 刊出日期:  2023-01-07

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