Deep Learning : Image Analysis and Transferable Knowledge in Computer Vision

Abstract

This short paper gives an overview of deep learning in computer vision with a focus on information that can be used in other situations. It shows how neural networks, especially Convolutional Neural Networks (CNNs), can learn on their own and compares them to older computer vision methods. It looks at the history of evolution, including important events like the 2012 ImageNet Competition and ground-breaking networks like ResNet, VGGNet, AlexNet, and GoogLeNet. In the paper, real-life examples from healthcare, self-driving cars, and security are used to show how deep learning has changed picture analysis. Deep learning architectures, including CNNs and RNNs, are talked about in depth, including important parts like filters, stride, and activation functions. Transfer learning is presented as an important method, and its three types are grouped: fine-tuning, feature extraction, and domain adaptation. It is emphasized that transfer learning has many benefits, such as better generalization, faster convergence, and more efficient use of data. The conclusion stresses how important transfer learning and fine-tuning are for making computer vision apps better. It shows how important it is to understand deep learning models and how they can be used in real life, as well as how they affect accuracy, efficiency, and generalization in the field of computer vision.

Keywords: Deep Learning, Computer Vision, Neural Network, Convolutional Neural Networks, ImageNet.