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Neural networks with model compression

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural networks with model compressionby Baochang Zhang ... [et al.].
other author:	Zhang, Baochang.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	ix, 260 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science)
Online resource:	https://doi.org/10.1007/978-981-99-5068-3
ISBN:	9789819950683$q(electronic bk.)

Neural networks with model compression
Neural networks with model compression[electronic resource] /by Baochang Zhang ... [et al.]. - Singapore :Springer Nature Singapore :2024. - ix, 260 p. :ill., digital ;24 cm. - Computational intelligence methods and applications,2510-1773. - Computational intelligence methods and applications..

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.

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.

ISBN: 9789819950683$q(electronic bk.)

Standard No.: 10.1007/978-981-99-5068-3doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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