Neural Networks with Model Compression / by Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann.

Format:

Book

Author/Creator:

Zhang, Baochang, author.

Series:

Computational Intelligence Methods and Applications, 2510-1773

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Image processing--Digital techniques.

Image processing.

Computer vision.

Machine Learning.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Local Subjects:

Machine Learning.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Physical Description:

1 online resource (267 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.

Contents:

Chapter 1. Introduction

Chapter 2. Binary Neural Networks

Chapter 3. Binary Neural Architecture Search

Chapter 4. Quantization of Neural Networks

Chapter 5. Network Pruning

Chapter 6. Applications.

Notes:

Includes bibliographical references.

ISBN:

9789819950683

9819950686

OCLC:

1420630388