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深度长尾学习研究综述

韩佳艺 刘建伟 陈德华 徐璟东 代琪 夏鹏飞

韩佳艺, 刘建伟, 陈德华, 徐璟东, 代琪, 夏鹏飞. 深度长尾学习研究综述. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240077
引用本文: 韩佳艺, 刘建伟, 陈德华, 徐璟东, 代琪, 夏鹏飞. 深度长尾学习研究综述. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240077
Han Jia-Yi, Liu Jian-Wei, Chen De-Hua, Xu Jing-Dong, Dai Qi, Xia Peng-Fei. Survey on deep long-tailed learning. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240077
Citation: Han Jia-Yi, Liu Jian-Wei, Chen De-Hua, Xu Jing-Dong, Dai Qi, Xia Peng-Fei. Survey on deep long-tailed learning. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240077

深度长尾学习研究综述

doi: 10.16383/j.aas.c240077 cstr: 32138.14.j.aas.c240077
详细信息
    作者简介:

    韩佳艺:中国石油大学(北京)人工智能学院自动化系博士研究生. 主要研究方向为深度长尾学习与计算机视觉. E-mail: 864494560@qq.com

    刘建伟:中国石油大学(北京)人工智能学院自动化系副教授. 2006年获得东华大学博士学位. 主要研究方向为智能系统, 机器学习, 复杂非线性系统的分析、预测与控制. E-mail: liujw@cup.edu.cn

    陈德华:东华大学计算机科学与技术学院教授. 主要研究方向为数据科学, 深度学习等. 本文通信作者. E-mail: chendehua@dhu.edu.cn

    徐璟东:中国石油大学(北京)人工智能学院自动化系硕士研究生. 主要研究方向为深度长尾学习, 因果推断. E-mail: 2948473452@qq.com

    代琪:中国石油大学(北京)人工智能学院自动化系博士. 2024年毕业于中国石油大学信息科学与工程学院自动化系控制理论与控制工程专业获博士学位. 主要研究方向为数据挖掘和机器学习

    夏鹏飞:东华大学计算机科学与技术学院博士研究生. E-mail: x6635570@163.com

Survey on deep long-tailed learning

More Information
    Author Bio:

    HAN Jia-Yi Ph. D.candidate at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. Her research interests include deep long-tailed learning and computer vision

    LIU Jian-Wei Associate Professor at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. He received the Ph.D. degree in control theory and control engineering from DongHua University in 2006. His research interests include pattern recognition and intelligent Systems, machine learning, analysis, prediction and control of complex non-linear system

    CHEN De-Hua Professor at the Department of computer science and technology, Donghua University. His research interests include data science and deep learning. Corresponding author of this paper

    XU Jing-Dong Master student at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. His research interests include deep long-tailed learning and causal inference

    Dai Qi Ph. D. at the Department of Automation, college of Artificial Intelligence, China University of Petroleum, Beijing. He received his Ph.D. degree in Control Theory and Control Engineering from China University of Petroleum, Beijing in 2024. His research interests include data mining and machine learning

    CHEN De-Hua Ph. D.candidate at the Department of computer science and technology, Donghua University

  • 摘要: 深度学习是一门依赖于数据的科学, 传统深度学习方法假定在平衡数据集上训练模型, 然而, 现实世界中大规模数据集通常表现出长尾分布现象, 样本数量众多的少量头部类主导模型训练, 而大量尾部类样本数量过少, 难以得到充分学习. 近年来, 长尾学习掀起学术界的研究热潮, 涌现出大量先进的工作. 本文综合梳理和分析了近年来发表在高水平会议或期刊上的文献, 对长尾学习进行全面的综述. 具体而言, 根据深度学习模型设计流程, 将图像识别领域的长尾学习算法分为丰富样本数量与语义信息的优化样本空间方法, 关注特征提取器、分类器、logits和损失函数这四个基本组成部分的优化模型方法以及通过引入帮助模型训练的辅助任务, 在多个空间共同优化长尾学习模型的辅助任务学习3大类, 并根据提出的分类方法综合对比分析每类长尾学习方法的优缺点. 然后, 进一步将基于样本数量的狭义长尾学习概念推广至多尺度广义长尾学习. 此外, 本文对文本数据、语音数据等其它数据形式下的长尾学习算法进行简要评述. 最后, 讨论了目前长尾学习面临的可解释性较差、数据质量较低等挑战, 并展望了如多模态长尾学习、半监督长尾学习等未来具有潜力的发展方向.
  • 图  1  深度长尾学习研究综述组织结构图

    Fig.  1  Organizational Structure Diagram of a Survey on Deep Long-Tail Learning Research

    图  2  长尾训练集示意图

    Fig.  2  Illustration of Long-Tail Training Set

    图  3  长尾测试集示意图

    Fig.  3  Illustration of Long-Tail Testing Set

    图  4  常用长尾数据集分布

    Fig.  4  Distributions of Common Long-Tail Datasets

    图  6  神经网络结构示意图

    Fig.  6  Diagram of Neural Network Architecture

    图  5  长尾图像识别研究现状

    Fig.  5  Current Status of Long-Tail Image Recognition Research

    图  8  优化样本空间各方法关系示意图

    Fig.  8  Diagram of Relationships Among Various Methods for Optimizing Sample Space

    图  7  重采样示意图

    Fig.  7  Diagram of Resampling

    图  9  单样本变换示意图

    Fig.  9  Diagram of Single Sample Transformation

    图  10  多样本变换方法示意图

    Fig.  10  Diagram of Multiple Sample Transformation Methods

    图  11  背景增强示意图

    Fig.  11  Background Enhancement Diagram

    图  12  语义增强样例图

    Fig.  12  Example Diagram of Semantic Enhancement

    图  13  优化模型空间

    Fig.  13  Optimized Model Space

    图  14  辅助任务学习各方法关系示意图

    Fig.  14  Diagram of the Relationships Among Various Methods in Auxiliary Task Learning

    图  15  两阶段解耦学习模型示意图[1]

    Fig.  15  Diagram of the Two-Stage Decoupled Learning Model[1]

    图  16  双分支网络(Bilateral-Branch Network, BBN)结构示意图[14]

    Fig.  16  Diagram of the Bilateral-Branch Network (BBN) Architecture[14]

    图  17  Range loss示意图[40]

    Fig.  17  Diagram of Range Loss[40]

    图  18  三阶段长尾知识蒸馏模型[147]

    Fig.  18  Three-Stage Long-Tail Knowledge Distillation Model[147]

    图  19  长尾集成学习模型示意图

    Fig.  19  Diagram of Long-Tail Ensemble Learning Model

    图  20  类间样本数量长尾分布与类内属性长尾分布示例图

    Fig.  20  Example Diagram of Long-Tail Distribution of Inter-Class Sample Counts and Intra-Class Attributes

    表  1  常见长尾数据集基本信息

    Table  1  Basic Information of Common Long-Tail Datasets

    类型数据集类别数量训练集样本数量测试集样本数量最大类样本数量最小类样本数量
    图像分类CIFAR10-LT[13]10500001000050005($ \rho$=100), 50($ \rho$=10)
    图像分类CIFAR100-LT[13]10050000100005005($ \rho$=100), 50($ \rho$=10)
    目标检测ImageNet-LT[62]10001158465000012805
    场景识别Places-LT[62]365625003650049805
    人脸识别MS1M-LT[62]74500(ID)88753035305981
    目标检测iNaturalist2017[63]5089579184182707196613381
    目标检测iNaturalist 2018[63]81424375132442612755119
    实例分割LVIS v0.5[64]12305700020000261481
    实例分割LVIS v1[64]120310017019822505521
    场景理解SUN-LT[65]39740842868122
    目标检测AWA-LT[65]50671360927202
    鸟类识别CUB-LT[65]20029452348432或3
    图像分类STL10-LT[66]10500080005005($ \rho$=100), 50($ \rho$=10)
    目标检测VOC-LT[67]20114249527754
    视频分类VideoLT[68]100417935251244191244
    下载: 导出CSV

    表  2  长尾图像识别方法比较

    Table  2  Comparison of Long-Tail Image Recognition Methods

    分类 代表性文献 优点 缺点
    优化样本空间 重采样 [1, 2, 56, 80, 70, 82, 30, 169] 简单通用, 理论直观, 易于操作 1)会丢弃大量头部类有效信息
    2)重复采样尾部类不能增加有效信息, 并容易引发过拟合
    3)易引入其它噪声
    数据增强 [2, 8, 9, 15, 76, 88, 89, 94, 95] 样本变换法成本较低, 易与其它方法结合, 灵活性较高. 语义增强法丰富尾部样本的语义信息, 生成具有现实意义的新样本 1)样本变换法引入大量新数据, 增加模型训练成本, 且可能生成毫无意义的样本, 鲁棒性较差.
    2)语义增强方法需设计专门的模型结构, 操作复杂. 并过于依赖于头部类数据质量, 易给模型带来新的偏置.
    优化模型空间 优化特征提取器 [107, 108, 109, 111, 112, 170] 有效增强样本上下文语义特征帮助模型学到无偏的特征表示 1)引入大量参数, 占用内存, 降低训练效率
    2)可解释性较差
    优化分类器 [1, 16, 26, 113, 115, 116, 118, 119] 计算开销小, 训练稳定无需设计额外的损失函数或存储单元 1)对超参数和优化器的选择敏感, 试错代价高
    2)灵活性较低, 在目标检测与实例分割任务上表现不佳
    logits调整 [12, 28, 30, 55, 71, 120, 122] 既能优化训练过程, 又能进行事后修正. 计算开销较低, 泛化性能良好, 易与其它方法结合. 1)依赖于数据集的先验分布
    2)修正后的边际分布可能不满足期望分布.
    代价敏感加权损失函数 [11, 12, 54, 72, 127, 129, 133] 操作简单, 易于实现, 计算开销较小, 适应于实际应用场景 1)优化困难, 参数敏感, 难以处理大规模真实场景
    2)头尾性能像“跷跷板”, 无法从本质上解决信息缺乏的问题
    辅助任务学习 解耦学习 [1, 14, 134, 135, 138, 139] 利用大量头部类数据生成泛化能力良好的特征表示能够有效提升模型性能, 且计算成本较低. 1)两阶段方法不利于端到端的模型训练与部署
    2)对数据依赖性较强
    3)与其它算法结合使用时需重新设计, 实用性不强
    度量学习 [40, 58, 59, 127, 145, 149, 151] 便于公式化与计算构建一个正样本接近, 负样本远离的特征空间, 优化决策边界. 1)尾部类样本极少的情况下性能很差.
    2)依赖于度量损失函数的设计
    知识蒸馏 [17, 19, 36, 145, 147, 154] 重用模型资源, 充分利用数据集蕴含的知识. 稳定尾部类学习过程 1)计算开销大, 优化成本相对过高, 对超参数敏感
    2)易出现师生不匹配问题, 整体性能过于依赖教师模型的学习情况
    集成学习 [18, 19, 20, 158, 159, 161] 在头部类和尾部类上都能保持良好性能泛化能力良好, 能够处理未知分布的测试集 1)计算和存储负担过大, 框架部署复杂
    2)专家之间存在相互影响的情况, 难以有效整合专家
    层次学习 [23, 24, 25, 162] 对数据间的关系进行多粒度建模, 捕捉类间隐式语义关系有助于头尾知识迁移 1)模型设计复杂, 训练成本较高
    2)依赖于高质量数据, 有时需要数据集提供外部信息
    3)层次划分步骤对后续训练产生过大影响
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-02-04
  • 录用日期:  2024-07-23
  • 网络出版日期:  2024-10-24

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