一种基于多尺度的多层卷积稀疏编码网络

谢伟立; 张军

doi:10.12052/gdutxb.230205

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一种基于多尺度的多层卷积稀疏编码网络

信息与通信工程 | 更新时间：2026-05-27

- 一种基于多尺度的多层卷积稀疏编码网络
- A Multi-layer Convolutional Sparse Coding Network Based on Multi-Scale
- 广东工业大学学报 2024年41卷第6期页码：125-132
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
  
  谢伟立(1999– ) ，男，硕士研究生，主要研究方向为卷积稀疏编码、深度学习等，E-mail：tlich@sina.com
  张军(1979– ) ，男，教授，博士，主要研究方向为人工智能技术、压缩感知理论及其应用，E-mail：jzhang@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61973088)
- DOI：10.12052/gdutxb.230205
  中图分类号： TP391
- 纸质出版：2024
- 稿件说明：
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谢伟立, 张军. 一种基于多尺度的多层卷积稀疏编码网络[J]. 广东工业大学学报, 2024, 41(6): 125-132.

Xie Wei-li, Zhang Jun. A Multi-layer Convolutional Sparse Coding Network Based on Multi-Scale[J]. 2024, 41(6): 125-132.
谢伟立, 张军. 一种基于多尺度的多层卷积稀疏编码网络[J]. 广东工业大学学报, 2024, 41(6): 125-132. DOI： 10.12052/gdutxb.230205.

Xie Wei-li, Zhang Jun. A Multi-layer Convolutional Sparse Coding Network Based on Multi-Scale[J]. 2024, 41(6): 125-132. DOI： 10.12052/gdutxb.230205.

摘要

多层卷积稀疏编码模型(Multi-layer Convolutional Sparse Coding

ML-CSC)被认为是对卷积神经网络(Convolutional Neural Networks

CNN)的一种理论阐释。尽管ML-CSC模型在特征对比度高的数据集上表现良好，但是其在特征对比度低的数据集上表现不佳。为了解决这一问题，本文引入多尺度技术设计了一种多尺度多层卷积稀疏编码网络(Multi-scale Multi-layer Convolutional Sparse Coding Network

MSMCSCNet)，不仅在特征对比度较弱的情况下得到更好的图像分类效果，而且也使模型具有扎实的理论基础和较高的可解释性。实验结果表明，MSMCSCNet在不增加参数量的前提下，在Cifar10、Cifar100数据集和Imagenet32数据子集上，准确率相比现有ML-CSC模型分别提高了5.75，9.75和9.8个百分点。此外，消融实验进一步证实了模型的多尺度设计和特征筛选模式设计的有效性。

Abstract

In recent years

the Multi-layer convolutional sparse coding (ML-CSC) model has been regarded as a theoretical explanation for convolutional neural networks (CNN). While the ML-CSC model performs well on datasets with high feature contrast

its performance is not satisfactory on datasets with low feature contrast. To address this issue

this paper introduces a multi-scale technique to design a multi-scale multi-layer convolutional sparse coding network (MSMCSCNet)

which not only achieves better image classification results in scenarios with weak feature contrast

but also provides the model with a solid theoretical foundation and higher interpretability. Experimental results demonstrate that

without increasing the parameter count

MSMCSCNet achieves accuracy improvements of 5.75

9.75

and 9.8 percentage points on the Cifar10

Cifar100 datasets

and the Imagenet32 subset

respectively

compared to existing ML-CSC models. Furthermore

ablation experiments further validate the effectiveness of the model's multi-scale design and feature selection mechanism.

关键词

Keywords

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