数据增强的深度卷积网络不变性机理的可解释性研究

刘妮; 雷娇娇; 邢振蓉; 韩文静; 何立火

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数据增强的深度卷积网络不变性机理的可解释性研究

更新时间：2026-08-31

- 数据增强的深度卷积网络不变性机理的可解释性研究
- Interpreting the Invariance Mechanism of Data Augmentation on Deep Convolutional Networks
- 西安交通大学学报 2026年
- 作者机构：
  
  1.长安大学信息工程学院，710064，西安
  2.西安电子科技大学电子工程学院，710071，西安
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TP18
- 收稿：2025-10-13，
  
  修回：2026-03-30，
  
  录用：2026-04-27，
- 稿件说明：
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刘妮, 雷娇娇, 邢振蓉, 等. 数据增强的深度卷积网络不变性机理的可解释性研究[J]. 西安交通大学学报,2026.

LIU Ni, LEI Jiao Jiao, XING ZHEN RONG, et al. Interpreting the Invariance Mechanism of Data Augmentation on Deep Convolutional Networks[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026.
刘妮, 雷娇娇, 邢振蓉, 等. 数据增强的深度卷积网络不变性机理的可解释性研究[J]. 西安交通大学学报,2026. DOI：

LIU Ni, LEI Jiao Jiao, XING ZHEN RONG, et al. Interpreting the Invariance Mechanism of Data Augmentation on Deep Convolutional Networks[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026. DOI：

摘要

在图像分类任务中，数据增强是提升深度卷积网络（ConvNets）分类不变性的通用手段。针对当前深度卷积网络不变性研究中主要聚焦网络层的整体不变性度量，而忽视了位置、方向等信息的表征机制，且缺乏对特征综合机理的系统阐释的问题，本文受认知神经科学领域相关研究的启发，采用圆形/方形二分类任务构建简化实验场景，有效规避复杂特征的干扰。采用最大激活方法，对全连接层与全局平均池化（AvgPool）层进行特征可视化分析，研究了经数据增强训练后的全连接层与AvgPool层的不变性学习特性，并采用t分布随机邻域嵌入（t-SNE）降维可视化揭示了层级间高维权重向量的分类决策机理。结果表明：（1）全连接层首层通过逐步扩大位置感知域的方式实现了平移不变性；（2）全连接层通过线性加权机制有效整合分布式局部特征，遵循同类别特性正向激发、异类别特征负向抑制的连接逻辑；（3）ResNet架构中AvgPool层展现更优的平移不变性，而全连接结构则表现出更强的旋转和缩放不变性。

Abstract

In image classification tasks

data augmentation is a common means to improve the classification invariance of deep convolutional networks (ConvNets ).To address the problems that current research on Convolutional Neural Network invariance primarily focuses on global metrics while neglecting the representation mechanisms of position and direction information

and lacks a systematic explanation of feature integration mechanisms

this study

inspired by relevant research in cognitive science and neuroscience

constructed a simplified experimental scenario using a circular/square binary classification task to effectively eliminate interference from complex features. By employing the maximum activation method

we visualized and analyzed the feature representation and invariance learning characteristics of the fully connected (FC) layers and Average Pooling (AvgPool) layers in models trained with data augmentation. Additionally

t-SNE dimensionality reduction visualization was used to reveal the classification decision-making mechanisms of high-dimensional weight vectors between layers. The results demonstrate that: (1) the initial fully connected layer achieves translation invariance by gradually expanding the position-aware domain; (2) the fully connected layer effectively integrates distributed local features through a linear weighting mechanism

following a connectivity logic of positive excitation for intra-class features and negative inhibition for inter-class features; and (3) the AvgPool layer in the ResNet architecture exhibits superior translation invariance

while the cascaded fully connected structure demonstrates stronger rotation and scaling invariance.

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