Deep Learning Architectures Explained: ResNet, InceptionV3, SqueezeNet

Introduction

Deep learning has completely transformed the way computers view and recognize images. Deep learning architectures help machines classify images, predict objects in an image, and even generate realistic visuals. The base of these powerful deep learning models lies in dense neural network architectures, which are designed for optimal performance, accuracy, and efficiency. In this article, we will explore the three most widely used Convolutional Neural Networks: ResNet, InceptionV3, and SqueezeNet. We will learn how they work, their key innovations, and their impact on deep learning applications.

ResNet: ResNet, also known as Residual Networks, was proposed by Microsoft in 2015 and brought a breakthrough to address the vanishing/exploding gradient issue in deep learning networks. Traditional deep learning models struggled with training as they became increasingly dense due to gradient degradation, making it extremely difficult to learn effectively as the number of layers increased. ResNet introduced skip connections (residual connections) that allow the gradient to bypass multiple layers and form residual blocks, making it possible to train extremely deep networks like ResNet-50 and ResNet-101 without significant performance loss. InceptionV3: Google developed Inceptionv3, an improved version of the Inception architecture, in 2016. The architecture is designed to optimize computation using parallel convolutional filters of different sizes.