04169nam a22006015i 4500 978-981-99-5068-3 DE-He213 20251030111415.0 cr nn 008mamaa 240205s2024 si | s |||| 0|eng d 9789819950683 978-981-99-5068-3 10.1007/978-981-99-5068-3 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Zhang, Baochang. author. aut http://id.loc.gov/vocabulary/relators/aut 65306 Neural Networks with Model Compression [electronic resource] / by Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. IX, 260 p. 101 illus., 67 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Intelligence Methods and Applications, 2510-1773 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. Machine learning. 55059 Artificial intelligence. 58441 Image processing Digital techniques. 64228 Computer vision. Machine Learning. 55059 Artificial Intelligence. 58441 Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Computer Vision. Wang, Tiancheng. author. aut http://id.loc.gov/vocabulary/relators/aut 65311 Xu, Sheng. author. aut http://id.loc.gov/vocabulary/relators/aut 65309 Doermann, David. author. aut http://id.loc.gov/vocabulary/relators/aut 65312 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819950676 Printed edition: 9789819950690 Printed edition: 9789819950706 Computational Intelligence Methods and Applications, 2510-1773 70583 https://doi.org/10.1007/978-981-99-5068-3 ZDB-2-SCS ZDB-2-SXCS 57481 57481