Gradient Re-Weighting Guided by Feature Fusion for Online Continual Learning

QIU Ben-liu; WANG Lan-xiao; QIU He-qian; GAO Xiang-yu; WEN Hai-tao; LI Hong-liang

doi:10.12263/DZXB.20250413

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Gradient Re-Weighting Guided by Feature Fusion for Online Continual Learning

PAPERS | 更新时间：2026-02-10

- Gradient Re-Weighting Guided by Feature Fusion for Online Continual Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 3970-3982(2025)
- 作者机构：
  
  电子科技大学信息与通信工程学院，四川成都 611731
- 作者简介：
- 基金信息：
  
  National Science and Technology Major Project(2021ZD0112001)
- DOI：10.12263/DZXB.20250413
  CLC： TP181;TP391.4
- Received：24 May 2025，
  
  Accepted：20 November 2025，
  
  Published：25 November 2025
- 稿件说明：
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邱奔流, 王岚晓, 邱荷茜, 等. 面向在线连续学习的特征融合引导的梯度重加权算法[J]. 电子学报, 2025, 53(11): 3970-3982.

QIU Ben-liu, WANG Lan-xiao, QIU He-qian, et al. Gradient Re-Weighting Guided by Feature Fusion for Online Continual Learning[J]. Acta Electronica Sinica, 2025, 53(11): 3970-3982.
邱奔流, 王岚晓, 邱荷茜, 等. 面向在线连续学习的特征融合引导的梯度重加权算法[J]. 电子学报, 2025, 53(11): 3970-3982. DOI：10.12263/DZXB.20250413

QIU Ben-liu, WANG Lan-xiao, QIU He-qian, et al. Gradient Re-Weighting Guided by Feature Fusion for Online Continual Learning[J]. Acta Electronica Sinica, 2025, 53(11): 3970-3982. DOI：10.12263/DZXB.20250413

摘要

在线连续学习（Online Continual Learning，OCL）旨在从非平稳的数据流中以仅仅读取一次数据样本的方式学习知识，因此面临着学习不充分的问题.为缓解这一问题，本文提出了一种特征融合的方法.该方法提取每张图片的一系列增强后样本的特征作为锚点特征，并通过加权求和的操作来融合这些特征以获得融合特征.融合权值由锚点特征和选定的作为枢纽特征的图片特征之间的相似性来决定.优化这一融合特征的交叉熵损失能够促进学习进程，进而在当前新任务上取得更好的表现.另外，我们提出了一致性损失来限制融合特征和枢纽特征之间的均方误差，以进一步提高模型在新任务上的表现.最后，我们理论分析了交叉熵损失关于模型参数的梯度.这一分析揭示了特征融合和梯度重加权之间的关系.我们选择了在线连续学习的三个常用基准进行了大量的实验，包括CIFAR-10、CIFAR-100和Tiny-ImageNet.相比基准方法，本文方法的平均最终准确率在CIFAR-10上提高了至多7.00%，在CIFAR-100上提高了至多8.04%，在Tiny-ImageNet上提高了至多6.33%.实验结果表明了本文方法的有效性，并且其在线连续学习能力相比已有方法取得了实质性的提升.

Abstract

Online continual learning (OCL) aims at learning a non-stationary data stream in a way of reading each data sample only once

and hence suffers from insufficient learning. To address this problem

we propose a feature fusion method in this work. Our method leverages augmented samples of an image for producing anchor features

and incorporates them to obtain a fused feature via a weighted summation operation. The weights are determined by the similarity between anchor features and a pre-designated pivotal feature of the image. Optimizing the cross-entropy loss of this fused feature can accelerate the learning process

resulting in better performance on the current task. Additionally

we propose a consistency loss that restricts the mean-square error between the fused feature and the pivotal feature

which can further improve the performance on the current task. Finally

we provide a theoretical analysis about the gradients of cross-entropy loss to model parameters. This analysis reveals the relationship between the feature fusion and the gradient re-weighting. Extensive experiments are conducted on three benchmarks under OCL settings

including CIFAR-10

CIFAR-100 and Tiny-ImageNet. Our method surpasses baselines at most 7.00%

8.04%

6.33% for average end accuracy on CIFAR-10

CIFAR-100 and Tiny-ImageNet

respectively. Experimental results demonstrate the proposed method is effective

and achieves substantial improvement over previous methods for online continual learning.

关键词

Keywords

references

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