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

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

1.
中国石油大学(北京)人工智能学院自动化系北京 102249
2.
东华大学计算机科学与技术学院上海 201620

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, 计算机视觉. E-mail: 2022310703@student.cup.edu.cn

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. 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: 2022211221@student.cup.edu.cn

代琪：2024年获得中国石油大学(北京)控制理论与控制工程专业博士学位. 主要研究方向为数据挖掘, 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. 主要研究方向为深度学习, 计算机视觉.E-mail: x6635570@163.com

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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

Survey on Deep Long-tailed Learning

1.
Department of Automation, College of Artificial Intelligence, China University of Petroleum, Beijing 102249
2.
College of Computer Science and Technology, Donghua University, Shanghai 201620

More Information

Author Bio:
HAN Jia-Yi　Ph.D. candidate in the Department of Automation, College of Artificial Intelligence, China University of Petroleum, Beijing. Her research interest covers deep long-tailed learning and computer vision

LIU Jian-Wei　Associate professor in the Department of Automation, College of Artificial Intelligence, China University of Petroleum, Beijing. He received his Ph.D. degree from Donghua University in 2006. His research interest covers intelligent system, machine learning, analysis, prediction and control of complex non-linear system

CHEN De-Hua　Professor at the College of Computer Science and Technology, Donghua University. His research interest covers data science and deep learning. Corresponding author of this paper

XU Jing-Dong　Master student in the Department of Automation, College of Artificial Intelligence, China University of Petroleum, Beijing. His research interest covers deep long-tailed learning and causal inference

DAI Qi　Received his Ph.D. degree in control theory and control engineering from China University of Petroleum, Beijing in 2024. His research interest covers data mining and machine learning

XIA Peng-Fei　Ph.D. candidate at the College of Computer Science and Technology, Donghua University. His research interest covers deep learning and computer vision

摘要

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. 本文综合梳理和分析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, 以及通过引入辅助任务来帮助模型训练并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, 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, 对文本数据、语音数据等其他数据形式下的长尾学习算法进行简要评述. 最后, 讨论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.
- 深度长尾学习 /
- 长尾分布 /
- 不平衡学习 /
- 深度学习
Abstract: Deep learning is a science that depends on data. Traditional deep learning methods unrealistically assume that the training models are on balanced datasets. In real-world large-scale datasets, a long-tailed distribution often occurs, with a few head classes having many samples dominating model training, while many tail classes have too few samples to be adequately learned. In recent years, the long-tailed learning has set off a research upsurge in academic circles. In this paper, we comb and analyze the literature published in high-level conferences or journals to provide a comprehensive survey of long-tailed learning. Specifically, we categorize long-tailed learning algorithms in the field of image recognition into three main types according to the design process of deep learning models: Optimizing the sample space by enriching the quantity and semantic information of samples, optimizing the model by focusing on the four fundamental components of feature extractor, classifier, logits and loss function, and auxiliary task learning, which involves introducing auxiliary tasks to aid model training and jointly optimizing long-tailed learning models across multiple spaces. Additionally, a comprehensive comparative analysis of the strengths and weaknesses of each category is conducted based on the proposed classification method. We further extend the concept of narrow long-tail learning based on the number of samples to multi-scale generalized long-tailed learning. In addition, we briefly review long-tailed learning algorithms in other data forms, such as text data and voice data. Finally, we discuss the current challenges faced by long-tailed learning, such as poor interpretability and low data quality, and look forward to the future development directions, such as multimodal long-tailed learning and semi-supervised long-tailed learning.
- Deep long-tailed learning /
- long-tailed distribution /
- imbalanced learning /
- deep learning

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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-tailed learning

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图 2 长尾训练集示意图

Fig. 2 Illustration of long-tailed training set

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图 3 长尾测试集示意图

Fig. 3 Illustration of long-tailed testing set

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图 4 常用长尾数据集分布

Fig. 4 Distributions of common long-tailed datasets

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图 5 长尾图像识别研究现状

Fig. 5 Current status of long-tailed image recognition research

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图 6 神经网络结构示意图

Fig. 6 Diagram of neural network architecture

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图 7 优化样本空间各方法关系示意图

Fig. 7 Diagram of relationships among various methods for optimizing sample space

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图 8 重采样示意图

Fig. 8 Diagram of resampling

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图 9 单样本变换示意图

Fig. 9 Diagram of single sample transformation

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图 10 多样本变换方法示意图

Fig. 10 Diagram of multiple sample transformation methods

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图 11 背景增强示意图

Fig. 11 Background enhancement diagram

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图 12 语义增强样例图

Fig. 12 Example diagram of semantic enhancement

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图 13 优化模型空间

Fig. 13 Optimized model space

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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

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图 15 两阶段解耦学习模型示意图

Fig. 15 Diagram of the two-stage decoupled learning model

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图 16 双分支网络结构示意图

Fig. 16 Diagram of the BBN architecture

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图 17 Range loss示意图

Fig. 17 Diagram of Range loss

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图 18 三阶段长尾知识蒸馏模型

Fig. 18 Three-stage long-tailed knowledge distillation model

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图 19 长尾集成学习模型示意图

Fig. 19 Diagram of long-tailed ensemble learning model

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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-tailed distribution of inter-class sample counts and intra-class attributes

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表 1 常见长尾数据集基本信息

Table 1 Basic information of common long-tailed 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]	10	50000	10000	5000	$5\;( \rho=100)$, $50\;( \rho=10)$
图像分类	CIFAR100-LT^[13]	100	50000	10000	500	$5\;( \rho=100)$, $50\;(\rho=10)$
目标检测	ImageNet-LT^[66]	1000	115846	50000	1280	5
场景识别	Places-LT^[66]	365	62500	36500	4980	5
目标检测	iNaturalist2017^[69]	5089	579184	182707	196613	381
目标检测	iNaturalist2018^[69]	8142	437513	24426	127551	19
实例分割	LVIS v0.5^[70]	1230	57000	20000	26148	1
实例分割	LVIS v1^[70]	1203	100170	19822	50552	1
场景理解	SUN-LT^[71]	397	4084	2868	12	2
人脸识别	MS1M-LT^[66]	74500 (ID)	887530	3530	598	1
目标检测	AWA-LT^[71]	50	6713	6092	720	2
鸟类识别	CUB-LT^[71]	200	2945	2348	43	2或3
图像分类	STL10-LT^[72]	10	5000	8000	500	$5\;( \rho=100)$, $50\;( \rho=10)$
目标检测	VOC-LT^[73]	20	1142	4952	775	4
视频分类	VideoLT^[74]	1004	179352	51244	1912	44

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表 2 长尾图像识别方法比较

Table 2 Comparison of long-tailed 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, 30, 56, 63, 82, 84, 173]	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)易引入其他噪声
优化样本空间	数据增强	[2, 8−9, 15, 85, 92−93, 98−99]	样本变换法成本较低, 易与其他方法结合, 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	[111−113, 115−116, 174]	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, 117, 119−120, 122−123]	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, 64, 124, 126]	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, 易与其他方法结合	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, 65, 131, 133, 137]	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问题
辅助任务学习	解耦学习	[1, 14, 138−139, 142−143]	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)与其他算法结合使用时需重新设计, 实用性不强
	度量学习	[40, 58−59, 131, 149, 152, 154]	便于公式化与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, 149, 157, 159]	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	[18−20, 162−163, 165]	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	[23−25, 166]	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

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