计算机工程2025，Vol.51Issue(4)：149-157,9.DOI:10.19678/j.issn.1000-3428.0068885
基于特征可视化探究跳跃连接结构对深度神经网络特征提取的影响
Exploring the Impact of Skip Connection Structures on the Deep Neural Networks Feature Extraction Based on Feature Visualization
摘要
Abstract
Training deep neural networks without skip connection structures is challenging when the depth of the networks is high.Thus,to address optimization issues and enhance generalization performance,skip connection structures have been integrated into the most recent deep neural network models.However,the effect of skip connection structures on feature extraction in deep neural networks has not yet been clarified;in most cases,these models are considered black boxes.Toward the elucidation of this effect,this study focuses on perturbation-based methods and introduces a method called Grid-Shuffled Blurring(GSB).This method aims to reduce the fine-grained details within an image,while maintaining its overall color distribution and contour characteristics.This study employs the Activation Maximization(AM)method for feature visualization and the GSB perturbation method to analyze classic deep neural network models such as VGG 19,ResNet 50,and DenseNet 201 in image classification tasks,which have different levels of skip connection structures.Experimental results show that the neural networks without the skip connection structures extract only stronger features from images,resulting in fewer extracted features,whereas those with the skip connection structures extract more features from images,albeit weaker ones.Moreover,the skip connection structures cause the models to focus more on the local color distribution and global contours of images,rather than the detailed features of images.The more the skip connection structures,the stronger is the trend.关键词
深度神经网络/跳跃连接结构/特征可视化/激活最大化/扰动方法/可解释性Key words
deep neural network/skip connection structures/feature visualization/Activation Maximization(AM)/perturbation method/interpretability分类
信息技术与安全科学引用本文复制引用
郭佩林,张德,王怀秀..基于特征可视化探究跳跃连接结构对深度神经网络特征提取的影响[J].计算机工程,2025,51(4):149-157,9.基金项目
国家自然科学基金(62271035) （62271035）
北京市自然科学基金(4232021) （4232021）
北京建筑大学校设科研基金自然科学项目(ZF17072). （ZF17072）
