Neural networks with model compression / Baochang Zhang, Sheng Xu, Mingbao Lin, Tiancheng Wang, David Doermann.
| Author/creator | Zhang, Baochang |
| Format | Electronic |
| Publication Info | Boca Raton : CRC Press, 2024. |
| Description | pages cm |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Other author/creator | Xu, Sheng (Professor of automation science and electrical engineering) |
| Other author/creator | Lin, Mingbao. |
| Other author/creator | Wang, Tiancheng (Artificial intelligence researcher) |
| Other author/creator | Doermann, David S. (David Scott) |
| Series | Multimedia computing, communication and intelligence |
| Contents | Quantization of neural networks -- Algorithms for binary neural networks -- Binary neural architecture search -- Applications in natural language processing -- Applications in computer vision. |
| Abstract | "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"-- Provided by publisher. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2023030909 |
| ISBN | 9781032452487 (hardback) |
| ISBN | 9781032452500 (paperback) |
| ISBN | (ebook) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |