Binary neural networks : algorithms, architectures, and 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 floatingpoint operations (FLOPs)...
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| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
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Boca Raton, FL :
CRC Press,
2024.
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| Edition: | First edition. |
| Series: | Multimedia computing, communication and intelligence.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| 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 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 |
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| Physical Description: | 1 online resource (xii, 205 pages) |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781003816799 1003816797 9781003376132 1003376134 9781003816850 1003816851 |