TY  - BOOK
AU  - Zhang,Baochang
AU  - Wang,Tiancheng
AU  - Xu,Sheng
AU  - Doermann,David
ED  - SpringerLink (Online service)
TI  - Neural Networks with Model Compression
T2  - Computational Intelligence Methods and Applications,
SN  - 9789819950683
AV  - Q325.5-.7 
U1  - 006.31 23
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Machine learning
KW  - Artificial intelligence
KW  - Image processing
KW  - Digital techniques
KW  - Computer vision
KW  - Machine Learning
KW  - Artificial Intelligence
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Computer Vision
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-99-5068-3
ER  -