Effect of Convolutional Depth on Image Recognition Performance: VGG vs. ResNet vs. GoogLeNet

2026-02-09

AI Summary

This study investigates the genuine impact of network depth on image recognition performance by introducing the concept of “effective depth” to distinguish between nominal layer count and the actual depth through which information propagates. Under a unified training protocol, controlled comparative experiments are conducted across VGG, ResNet, and GoogLeNet architectures to quantitatively assess how depth influences accuracy, convergence stability, and computational efficiency. The findings reveal that residual connections and Inception modules substantially enhance depth utilization efficiency, achieving superior performance with lower effective depth, whereas conventional deep networks often suffer from accuracy saturation and optimization difficulties. This work underscores the critical role of architectural design in modulating the benefits of depth, moving beyond the simplistic paradigm of merely stacking more layers.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Increasing convolutional depth has been central to advances in image recognition, yet deeper networks do not uniformly yield higher accuracy, stable optimization, or efficient computation. We present a controlled comparative study of three canonical convolutional neural network architectures - VGG, ResNet, and GoogLeNet - to isolate how depth influences classification performance, convergence behavior, and computational efficiency. By standardizing training protocols and explicitly distinguishing between nominal and effective depth, we show that the benefits of depth depend critically on architectural mechanisms that constrain its effective manifestation during training rather than on nominal depth alone. Although plain deep networks exhibit early accuracy saturation and optimization instability, residual and inception-based architectures consistently translate additional depth into improved accuracy at lower effective depth and favorable accuracy-compute trade-offs. These findings demonstrate that effective depth, not nominal depth, is the operative quantity governing depth's role as a productive scaling dimension in convolutional networks.

Problem

• convolutional depth
• image recognition
• network architecture
• effective depth
• nominal depth

Innovation

• effective depth
• convolutional neural networks
• architectural mechanisms
• image recognition
• depth scaling