Overview of Popular CNN Models

Convolutional Neural Networks (CNNs) have significantly evolved, with various architectures improving accuracy, efficiency, and scalability. This chapter explores five key CNN models that have shaped deep learning: LeNet, AlexNet, VGGNet, ResNet, and InceptionNet.

LeNet: The Foundation of CNNs

Developed by Yann LeCun in 1998, LeNet was one of the first CNN architectures, designed for handwritten digit recognition. It introduced essential CNN concepts such as convolutional layers, pooling layers, and fully connected layers. LeNet consists of two convolutional layers followed by two fully connected layers, making it relatively simple yet highly effective for early image classification tasks. Though limited in depth and complexity, LeNet laid the groundwork for more advanced architectures that followed.

AlexNet: Deep Learning Breakthrough

AlexNet, which won the 2012 ImageNet competition, marked a major breakthrough in deep learning. This model demonstrated that deep CNNs could outperform traditional machine learning techniques for large-scale image classification. AlexNet consists of eight layers: five convolutional layers followed by three fully connected layers. It introduced key innovations such as ReLU activations to accelerate training, dropout regularization to prevent overfitting, and GPU acceleration, which enabled deeper networks to be trained efficiently. The success of AlexNet helped popularize deep learning across various domains.

VGGNet: Deeper Networks with Uniform Filters

VGGNet, developed by Oxford’s Visual Geometry Group, focused on building deeper networks with a consistent structure. Unlike AlexNet, which used larger filter sizes, VGGNet employed small 3×3 convolutional filters stacked together, demonstrating that increasing network depth improves feature extraction. VGG-16 and VGG-19, two of the most well-known variants, consist of 16 and 19 layers, respectively. Despite their superior performance, VGG models require high computational resources due to the large number of parameters.

ResNet: Solving the Depth Problem

ResNet (Residual Networks), introduced by Microsoft in 2015, addressed the vanishing gradient problem, which occurs when training very deep networks. Traditional deep networks struggle with training efficiency and performance degradation, but ResNet overcame this issue with skip connections (residual learning). These shortcuts allow information to bypass certain layers, ensuring that gradients continue to propagate effectively. ResNet architectures, such as ResNet-50 and ResNet-101, enabled the training of networks with hundreds of layers, significantly improving image classification accuracy.

InceptionNet: Multi-Scale Feature Extraction

InceptionNet, also known as GoogLeNet, introduced a novel approach to convolutional layers by incorporating the Inception module, which processes multiple receptive fields simultaneously. Unlike traditional architectures that use a fixed kernel size, InceptionNet applies multiple convolutional filters (1×1, 3×3, and 5×5) in parallel, capturing features at different scales. This design enhances feature extraction efficiency while reducing computational cost. Later versions, such as Inception-v3 and Inception-v4, further refined this approach by introducing batch normalization and additional optimization techniques.

Each of these CNN architectures has played a pivotal role in advancing computer vision, influencing applications in healthcare, autonomous systems, security, and real-time image processing. From LeNet’s foundational principles to InceptionNet’s multi-scale feature extraction, these models have continuously pushed the boundaries of deep learning, paving the way for even more advanced architectures in the future.