AI-Enhanced Deep Learning Architectures for Image Recognition

A Comparative Analysis of CNN Models with Modern AI Integration

Abstract

Convolutional Neural Networks (CNNs) have revolutionized image recognition by organizing features hierarchically and optimizing structures. In this paper, three popular CNN architectures (VGGNet, ResNet and GoogLeNet, also known as Inception) are compared in terms of their forward propagation, loss functions and back propagation. We delve deeper than that basic exploration and discuss the impact of current Artificial Intelligence (AI) developments such as Vision Transformers, self-supervised learning, and neural architecture search on the image recognition field. We analyze the computational efficiency, gradient flow, and scalability of each model, and illustrate that ResNet's residual connections provide the best of the three worlds of depth scalability, gradient stability and training efficiency. In addition, the emerging paradigms of AI like foundation models and multimodal learning are coming together with CNN-based solutions to shape the future generation of visual understanding systems. In this video, we will introduce you to the various neural network architectures that dominate the field of image recognition.In this video, we will introduce you to the various types of neural network architectures that dominate the field of image recognition: convolutional neural networks, deep learning networks, ResNet, VGGNet, GoogLeNet, Vision Transformers, self-supervised learning, and AI.