Binary Neural Networks
Algorithms, Architectures, and Applications
Series: Multimedia Computing, Communication and Intelligence;
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 13 December 2023
- ISBN 9781032452487
- Binding Hardback
- No. of pages215 pages
- Size 254x178 mm
- Weight 580 g
- Language English
- Illustrations 104 Illustrations, black & white; 19 Halftones, black & white; 85 Line drawings, black & white; 31 Tables, black & white 520
Categories
Short description:
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.
MoreLong description:
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 floatingpoint 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, Binary Neural Networks: Algorithms, Architectures, and Applications 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 and binary NAS and 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 of machine learning and deep learning to better understand the methods described in this book.
Key Features
- Reviews recent advances in CNN compression and acceleration
- Elaborates recent advances on binary neural network (BNN) technologies
- Introduces applications of BNN in image classification, speech recognition, object detection, and more
Table of Contents:
Chapter 1: Introduction.
Chapter 2: Quantization of Neural Networks.
Chapter 3: Algorithms for Binary Neural Networks.
Chapter 4: Binary Neural Architecture Search.
Chapter 5: Applications in Natural Language Processing.
Chapter 6: Applications in Computer Vision.
Bibliography.
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