Label noise adaptive learning algorithm based on meta-learning

doi:10.11772/j.issn.1001-9081.2024070932

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (7): 2113-2122.DOI: 10.11772/j.issn.1001-9081.2024070932

• The 39th CCF National Conference of Computer Applications (CCF NCCA 2024) • Previous Articles Next Articles

Label noise adaptive learning algorithm based on meta-learning

Qiaoling QI¹, Xiaoxiao WANG², Qianqian ZHANG³(), Peng WANG², Yongfeng DONG²^,⁴^,⁵

^1.School of Electrical Engineering，Hebei University of Technology，Tianjin 300130，China
^2.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^3.School of Energy and Environmental Engineering，Hebei University of Technology，Tianjin 300401，China
^4.Tianjin International Joint Center for Virtual Reality and Visual Computing，Tianjin 300401，China
^5.Hebei Data Driven Industrial Intelligent Engineering Research Center （Hebei University of Technology），Tianjin 300401，China

Received:2024-07-05 Revised:2024-10-15 Accepted:2024-10-16 Online:2025-07-10 Published:2025-07-10
Contact: Qianqian ZHANG
About author:QI Qiaoling， born in 1984， M. S.， lecturer. Her research interests include artificial intelligence.
WANG Xiaoxiao， born in 1998， M. S. candidate. Her research interests include image processing.
ZHANG Qianqian， born in 1987， M. S.， lecturer. Her research interests include innovation and entrepreneurship education.
WANG Peng， born in 1978， Ph. D.， associate professor. His research interests include machine learning， intelligent computing.
DONG Yongfeng， born in 1977， Ph. D.， professor. His research interests include computer vision， intelligent information processing.
Supported by:
Higher Education Scientific Research Planning of China Association of Higher Education Project(22XX0401);First Batch of Provincial Postgraduate Education and Teaching Reform Research Project in Hebei Province(YJG2023024)

基于元学习的标签噪声自适应学习算法

齐巧玲¹, 王啸啸², 张茜茜³(), 汪鹏², 董永峰²^,⁴^,⁵

^1.河北工业大学电气工程学院，天津 300130
^2.河北工业大学人工智能与数据科学学院，天津 300401
^3.河北工业大学能源与环境工程学院，天津 300401
^4.天津市虚拟现实与可视计算国际联合中心，天津 300401
^5.河北省数据驱动工业智能工程研究中心（河北工业大学），天津 300401

通讯作者: 张茜茜
作者简介:齐巧玲（1984—），女，河北晋州人，讲师，硕士，主要研究方向：人工智能
王啸啸（1998—），女，河北衡水人，硕士研究生，主要研究方向：图像处理
张茜茜（1987—），女，山东潍坊人，讲师，硕士，主要研究方向：创新创业教育 zhenghaibin29@buaa.edu.cn
汪鹏（1978—），男，河北邯郸人，副教授，博士，CCF会员，主要研究方向：机器学习、智能计算
董永峰（1977—），男，河北定州人，教授，博士，CCF会员，主要研究方向：计算机视觉、智能信息处理。
基金资助:
中国高等教育学会高等教育科学研究规划课题(22XX0401);河北省首批省级研究生教育教学改革研究项目(YJG2023024)

Abstract

Abstract:

Image classification requires the collection of a large number of images for model training and optimization， but the image collection process will bring noisy labels inevitably. To cope with this challenge， robust classification methods have emerged. The setting of hyperparameters in the current robust classification methods needs to be adjusted manually， which brings a lot of loss in human and material resources. Therefore， Meta Hyperparameter Adjuster （MHA） was proposed， which adopted a two-layer nested loop optimization method to learn noise-aware hyperparameter combinations adaptively， and a Meta-FPL （Feature Pseudo-Label adaptive learning algorithm based on Meta learning） algorithm was proposed too. In addition， in order to solve the problem that the backpropagation process in meta training phase consumes a large amount of GPU computing power， the Select Activation Metamodel Layer （SAML） strategy was proposed， which restricts the update of some metamodel layers by comparing sizes of the average gradient of the backpropagation and the meta-gradient in virtual training phase， which improves training efficiency of the model effectively. Experimental results on four benchmark datasets and one real dataset show that compared with MLC （Meta Label Correction for noisy label learning）， CTRR （ConTrastive RegulaRization） and Feature Pseudo Label （FPL） algorithms， Meta-FPL algorithm has a higher classification accuracy. In addition， after introducing SAML strategy， the training duration of the backpropagation process in the meta training phase was reduced by 79.52%. It can be seen that Meta-FPL algorithm can effectively improve the accuracy of classification testing in a shorter training time.

Key words: deep learning, deep neural network, image classification, label noise, meta-learning

摘要：

图像分类需要收集大量的图片进行模型训练与优化，但收集过程会不可避免地带来噪声标签。为了应对这一挑战，鲁棒性分类方法应运而生。在目前的鲁棒性分类方法中，超参数的设置需要手动调节，对人力物力带来了大量的损耗。因此，提出元超参数调节器（MHA），采用双层嵌套循环优化的方法自适应地学习噪声感知的超参数组合，并提出Meta-FPL （Feature Pseudo-Label adaptive learning based on Meta learning）算法。此外，为了解决元训练阶段的反向传播过程耗费大量GPU算力的问题，提出选择激活元模型层（SAML）策略。该策略通过比较虚拟训练阶段反向传播的平均梯度与元梯度的大小，限制部分元模型层的更新，从而有效提升模型的训练效率。在4个基准数据集和1个真实数据集上的实验结果表明，与MLC（Meta Label Correction for noisy label learning）、CTRR（ConTrastive RegulaRization）和FPL（Feature Pseudo-Label）算法相比，Meta-FPL算法的分类准确率较高。此外，引入SAML策略后，在元训练阶段的反向传播过程训练时长缩短了79.52%。可见，Meta-FPL算法能在较短的训练时间内有效提升分类测试准确率。

关键词: 深度学习, 深度神经网络, 图像分类, 标签噪声, 元学习

CLC Number:

TP391.4

Qiaoling QI, Xiaoxiao WANG, Qianqian ZHANG, Peng WANG, Yongfeng DONG. Label noise adaptive learning algorithm based on meta-learning[J]. Journal of Computer Applications, 2025, 45(7): 2113-2122.

齐巧玲, 王啸啸, 张茜茜, 汪鹏, 董永峰. 基于元学习的标签噪声自适应学习算法[J]. 《计算机应用》唯一官方网站, 2025, 45(7): 2113-2122.

Figures/Tables 13

References 48

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数据集	类别数	训练集样本数	测试集样本数	图片尺寸
MNIST	10	60 000	10 000	28×28
SVHN	10	7 325	26 032	32×32
CIFAR10	10	50 000	10 000	32×32
CIFAR100	100	50 000	10 000	32×32
Clothing1M	14	50 000	14 000	224×224

数据集	类别数	训练集样本数	测试集样本数	图片尺寸
MNIST	10	60 000	10 000	28×28
SVHN	10	7 325	26 032	32×32
CIFAR10	10	50 000	10 000	32×32
CIFAR100	100	50 000	10 000	32×32
Clothing1M	14	50 000	14 000	224×224

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	94.52±0.16	78.56±0.36	88.93±0.38	64.94±0.42	91.70±0.20	71.26±0.31	90.15±0.25	69.24±0.41
GCE	98.99±0.14	98.46±0.12	99.16±0.11	98.75±0.13	99.12±0.09	99.03±0.14	98.28±0.17	97.97±0.21
APL	99.53±0.03	99.29±0.03	99.22±0.05	80.01±0.43	99.41±0.04	97.22±0.04	99.26±0.04	87.47±0.44
ELR	99.22±0.13	98.97±0.16	99.09±0.10	98.99±0.14	99.09±0.09	99.06±0.15	99.05±0.11	98.94±0.17
MSLC	99.07±0.11	98.85±0.14	99.57±0.10	99.33±0.10	99.64±0.12	99.35±0.14	99.58±0.14	99.41±0.16
CDR	94.27±0.35	75.76±0.94	87.40±0.94	62.83±1.57	91.40±0.45	70.13±1.16	89.62±0.53	67.40±1.59
PES	99.57±0.04	99.49±0.05	99.62±0.02	99.58±0.03	99.69±0.01	99.54±0.03	99.51±0.02	99.42±0.04
MLC	98.80±0.04	98.47±0.07	98.94±0.05	97.36±0.06	98.91±0.01	98.54±0.03	98.94±0.03	98.42±0.05
CTRR	98.96±0.24	98.16±0.34	99.29±0.52	99.06±0.47	99.01±0.35	98.94±0.26	98.83±0.28	98.14±0.32
FPL	99.71±0.01	99.71±0.02	99.72±0.02	99.70±0.02	99.73±0.02	99.70±0.02	99.60±0.01	99.52±0.02
Meta-FPL	99.80±0.03	99.75±0.05	99.83±0.04	99.77±0.03	99.79±0.04	99.72±0.04	99.71±0.04	99.64±0.05

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	94.52±0.16	78.56±0.36	88.93±0.38	64.94±0.42	91.70±0.20	71.26±0.31	90.15±0.25	69.24±0.41
GCE	98.99±0.14	98.46±0.12	99.16±0.11	98.75±0.13	99.12±0.09	99.03±0.14	98.28±0.17	97.97±0.21
APL	99.53±0.03	99.29±0.03	99.22±0.05	80.01±0.43	99.41±0.04	97.22±0.04	99.26±0.04	87.47±0.44
ELR	99.22±0.13	98.97±0.16	99.09±0.10	98.99±0.14	99.09±0.09	99.06±0.15	99.05±0.11	98.94±0.17
MSLC	99.07±0.11	98.85±0.14	99.57±0.10	99.33±0.10	99.64±0.12	99.35±0.14	99.58±0.14	99.41±0.16
CDR	94.27±0.35	75.76±0.94	87.40±0.94	62.83±1.57	91.40±0.45	70.13±1.16	89.62±0.53	67.40±1.59
PES	99.57±0.04	99.49±0.05	99.62±0.02	99.58±0.03	99.69±0.01	99.54±0.03	99.51±0.02	99.42±0.04
MLC	98.80±0.04	98.47±0.07	98.94±0.05	97.36±0.06	98.91±0.01	98.54±0.03	98.94±0.03	98.42±0.05
CTRR	98.96±0.24	98.16±0.34	99.29±0.52	99.06±0.47	99.01±0.35	98.94±0.26	98.83±0.28	98.14±0.32
FPL	99.71±0.01	99.71±0.02	99.72±0.02	99.70±0.02	99.73±0.02	99.70±0.02	99.60±0.01	99.52±0.02
Meta-FPL	99.80±0.03	99.75±0.05	99.83±0.04	99.77±0.03	99.79±0.04	99.72±0.04	99.71±0.04	99.64±0.05

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	87.44±0.34	67.09±0.56	83.42±0.43	60.86±0.37	86.0±0.24	67.21±0.44	83.2±0.45	59.55±0.64
GCE	76.57±0.28	60.82±0.30	77.83±0.24	62.59±0.35	78.06±0.19	62.98±0.27	75.87±0.26	60.12±0.34
APL	77.46±0.31	61.05±0.46	75.62±0.45	59.96±0.47	76.06±0.34	60.53±0.41	75.27±0.44	59.83±0.45
ELR	74.39±0.28	56.19±0.23	76.94±0.22	57.43±0.26	75.94±0.28	57.16±0.21	75.69±0.32	58.16±0.27
MSLC	97.08±0.17	95.12±0.14	96.97±0.21	95.24±0.23	97.10±0.18	96.88±0.20	97.09±0.23	96.95±0.25
CDR	82.46±0.21	67.50±2.09	82.35±1.39	60.41±2.15	84.79±1.00	64.59±1.44	81.96±2.30	60.71±3.33
PES	87.63±0.23	69.31±0.37	88.92±0.25	70.26±0.31	89.17±0.19	70.34±0.27	85.49±0.21	67.38±0.36
MLC	96.75±0.06	95.96±0.12	96.99±0.11	96.06±0.10	96.43±0.09	95.81±0.11	96.38±0.12	95.24±0.15
CTRR	96.21±0.14	95.57±0.53	97.02±0.21	96.20±0.34	96.53±0.44	96.06±0.45	96.17±0.14	95.89±0.11
FPL	97.14±0.02	96.87±0.02	97.15±0.01	97.11±0.02	97.14±0.01	97.01±0.01	97.11±0.01	97.02±0.01
Meta-FPL	97.23±0.04	96.98±0.05	97.21±0.03	97.14±0.04	97.26±0.05	97.20±0.07	97.16±0.06	97.09±0.06

Label noise adaptive learning algorithm based on meta-learning

基于元学习的标签噪声自适应学习算法

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Figures/Tables 13

References 48

Related Articles 15

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Metrics

算法	最终	最佳
Standard	67.22	64.68
APL	56.01	55.84
ELR	73.27	74.53
MSLC	73.34	73.95
CDR	63.72	63.22
PES	72.37	73.64
MLC	73.28	75.61
CTRR	72.71	73.68
FPL	73.54	73.57
Meta-FPL	73.61	73.94

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算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	83.81±0.15	66.22±0.43	81.83±0.32	59.41±0.60	82.97±0.23	64.18±0.41	81.65±0.42	59.02±0.42
GCE	89.70±0.23	87.62±0.35	89.83±0.19	87.61±0.48	90.35±0.04	86.63±0.06	89.45±0.29	57.41±0.34
APL	88.15±0.09	81.51±0.17	86.07±0.09	51.66±0.20	77.34±0.23	32.66±0.26	78.83±2.11	60.00±1.71
ELR	89.42±0.11	87.52±0.10	89.87±0.10	89.36±0.16	88.96±0.09	88.48±0.14	89.78±0.13	90.05±0.17
MSLC	93.20±0.09	90.67±0.17	94.09±0.09	92.74±0.11	92.82±0.11	92.44±0.11	92.46±0.10	91.93±0.14
CDR	82.79±0.66	64.16±0.74	81.25±0.96	60.17±0.94	82.14±0.60	64.13±1.13	81.11±0.80	62.69±1.51
PES	92.48±0.06	89.17±0.13	92.14±0.03	88.77±0.09	92.40±0.05	88.70±0.08	91.57±0.07	89.27±0.12
MLC	89.46±0.12	86.53±0.24	90.33±0.15	90.30±0.14	90.31±0.15	90.29±0.13	89.74±0.13	89.51±0.14
CTRR	92.97±0.32	92.16±0.31	93.05±0.56	92.82±0.27	93.89±0.39	93.04±0.33	91.67±0.25	90.16±0.50
FPL	93.55±0.03	92.92±0.04	94.13±0.07	94.08±0.09	94.23±0.06	93.71±0.06	93.70±0.06	93.50±0.04
Meta-FPL	93.60±0.05	93.01±0.05	94.20±0.06	94.12±0.07	94.32±0.07	93.78±0.06	93.77±0.05	93.60±0.08

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	83.81±0.15	66.22±0.43	81.83±0.32	59.41±0.60	82.97±0.23	64.18±0.41	81.65±0.42	59.02±0.42
GCE	89.70±0.23	87.62±0.35	89.83±0.19	87.61±0.48	90.35±0.04	86.63±0.06	89.45±0.29	57.41±0.34
APL	88.15±0.09	81.51±0.17	86.07±0.09	51.66±0.20	77.34±0.23	32.66±0.26	78.83±2.11	60.00±1.71
ELR	89.42±0.11	87.52±0.10	89.87±0.10	89.36±0.16	88.96±0.09	88.48±0.14	89.78±0.13	90.05±0.17
MSLC	93.20±0.09	90.67±0.17	94.09±0.09	92.74±0.11	92.82±0.11	92.44±0.11	92.46±0.10	91.93±0.14
CDR	82.79±0.66	64.16±0.74	81.25±0.96	60.17±0.94	82.14±0.60	64.13±1.13	81.11±0.80	62.69±1.51
PES	92.48±0.06	89.17±0.13	92.14±0.03	88.77±0.09	92.40±0.05	88.70±0.08	91.57±0.07	89.27±0.12
MLC	89.46±0.12	86.53±0.24	90.33±0.15	90.30±0.14	90.31±0.15	90.29±0.13	89.74±0.13	89.51±0.14
CTRR	92.97±0.32	92.16±0.31	93.05±0.56	92.82±0.27	93.89±0.39	93.04±0.33	91.67±0.25	90.16±0.50
FPL	93.55±0.03	92.92±0.04	94.13±0.07	94.08±0.09	94.23±0.06	93.71±0.06	93.70±0.06	93.50±0.04
Meta-FPL	93.60±0.05	93.01±0.05	94.20±0.06	94.12±0.07	94.32±0.07	93.78±0.06	93.77±0.05	93.60±0.08

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	56.78±0.20	38.09±0.25	58.27±0.11	41.52±0.17	59.99±0.13	44.25±0.33	55.10±0.37	36.78±0.26
GCE	65.24±0.56	62.39± 0.38	66.21±0.09	57.62±0.12	64.79±0.11	58.35±0.13	61.87±0.39	47.66±0.69
APL	56.41±0.11	46.25±0.11	56.00±0.14	41.95±0.16	56.40±0.14	45.08±0.11	55.46±0.12	44.68±0.16
ELR	70.83±0.19	66.91±0.21	73.46±0.13	71.69±0.17	72.60±0.16	72.48±0.20	70.09±0.13	69.59±0.19
MSLC	71.47±0.20	67.50±0.14	72.62±0.13	71.79±0.24	72.33±0.08	71.71±0.12	70.82±0.11	69.04±0.15
CDR	58.45±0.27	52.56±0.34	63.62±0.26	45.82±0.25	65.39±0.31	48.89±0.35	61.70±0.56	43.00±0.85
PES	69.26±0.07	64.96±0.14	70.67±0.05	64.85±0.12	71.43±0.13	67.40±0.18	68.30±0.07	66.27±0.16
MLC	50.92±0.22	39.76±0.13	58.85±0.23	48.88±0.14	58.85±0.23	48.87±0.14	50.67±0.17	47.72±0.22
CTRR	70.09±0.45	65.32±0.20	72.88±0.34	66.75±0.28	73.12±0.42	64.46±0.17	70.15±0.23	64.53±0.17
FPL	71.55±0.08	67.84±0.04	73.43±0.09	72.35±0.09	73.26±0.03	72.62±0.07	71.49±0.05	70.34±0.05
Meta-FPL	71.58±0.06	67.86±0.07	73.47±0.07	72.41±0.09	73.24±0.05	72.68±0.08	71.44±0.06	70.38±0.05

算法	Symmetric		Pairflip		Tridiagonal		Instance
算法	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%	NR为20%	NR为40%
Standard	56.78±0.20	38.09±0.25	58.27±0.11	41.52±0.17	59.99±0.13	44.25±0.33	55.10±0.37	36.78±0.26
GCE	65.24±0.56	62.39± 0.38	66.21±0.09	57.62±0.12	64.79±0.11	58.35±0.13	61.87±0.39	47.66±0.69
APL	56.41±0.11	46.25±0.11	56.00±0.14	41.95±0.16	56.40±0.14	45.08±0.11	55.46±0.12	44.68±0.16
ELR	70.83±0.19	66.91±0.21	73.46±0.13	71.69±0.17	72.60±0.16	72.48±0.20	70.09±0.13	69.59±0.19
MSLC	71.47±0.20	67.50±0.14	72.62±0.13	71.79±0.24	72.33±0.08	71.71±0.12	70.82±0.11	69.04±0.15
CDR	58.45±0.27	52.56±0.34	63.62±0.26	45.82±0.25	65.39±0.31	48.89±0.35	61.70±0.56	43.00±0.85
PES	69.26±0.07	64.96±0.14	70.67±0.05	64.85±0.12	71.43±0.13	67.40±0.18	68.30±0.07	66.27±0.16
MLC	50.92±0.22	39.76±0.13	58.85±0.23	48.88±0.14	58.85±0.23	48.87±0.14	50.67±0.17	47.72±0.22
CTRR	70.09±0.45	65.32±0.20	72.88±0.34	66.75±0.28	73.12±0.42	64.46±0.17	70.15±0.23	64.53±0.17
FPL	71.55±0.08	67.84±0.04	73.43±0.09	72.35±0.09	73.26±0.03	72.62±0.07	71.49±0.05	70.34±0.05
Meta-FPL	71.58±0.06	67.86±0.07	73.47±0.07	72.41±0.09	73.24±0.05	72.68±0.08	71.44±0.06	70.38±0.05