A Brief Dive into Classic Convolutional Neural Networks: LeNet, AlexNet, VGG, ResNet, InceptionNet, and a Comparative Analysis

Introduction

Explore the evolution of Convolutional Neural Networks (CNNs) through a journey with some of the pioneering architectures — LeNet, AlexNet, VGG, ResNet, and InceptionNet. Each of these models has played a crucial role in advancing the field of computer vision. In this blog, we’ll take a concise look at their key features and conclude with a comparative analysis to highlight their strengths and weaknesses.

LeNet

LeNet, introduced by Yann LeCun in 1998, was among the first CNN architectures. Designed for handwritten digit recognition, it consists of convolutional and subsampling layers, followed by fully connected layers. While modest by today’s standards, LeNet set the foundation for future CNN development.

AlexNet

AlexNet, proposed by Alex Krizhevsky in 2012, marked a breakthrough by winning the ImageNet Large Scale Visual Recognition Challenge. It featured a deeper architecture, using rectified linear units (ReLUs) for activation functions and dropout for regularization. AlexNet showcased the potential of deep learning in image classification tasks.

VGG:

The Visual Geometry Group (VGG) introduced the VGGNet in 2014. VGG stood out for its simplicity with all 3x3 convolutional layers, and its depth contributed to improved feature representation. Despite its simplicity, VGG demonstrated strong performance on various image classification tasks.

ResNet:

ResNet, proposed by Kaiming He et al. in 2015, addressed the vanishing gradient problem by introducing skip connections. These skip connections enabled the training of very deep networks, with ResNet-152 being one of the most prominent versions. ResNet’s impact on deep learning was immense, leading to the widespread adoption of skip connections in subsequent architectures.

InceptionNet (GoogLeNet):

InceptionNet, or GoogLeNet, introduced by Google in 2014, featured the inception module, which used multiple filter sizes within the same layer. This allowed the network to capture multi-scale features efficiently. InceptionNet achieved high accuracy with reduced computational complexity, making it an influential architecture.

Comparison:

• Depth: ResNet has the advantage of extremely deep architectures with skip connections, overcoming the vanishing gradient problem.
• Computational Efficiency: InceptionNet achieves a balance between accuracy and computational efficiency, thanks to its inception module.
• Simplicity: VGG’s straightforward architecture is easy to understand and implement but may lack the efficiency of more complex models.
• Performance: AlexNet demonstrated the potential of deep learning, while ResNet’s skip connections further improved performance.
• Specialization: LeNet pioneered CNNs for specific tasks like digit recognition, while the others are more versatile for broader image classification tasks.