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Titlebook: Neural Networks with Model Compression; Baochang Zhang,Tiancheng Wang,David Doermann Book 2024 The Editor(s) (if applicable) and The Autho

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發(fā)表于 2025-3-21 16:30:10 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Networks with Model Compression
編輯Baochang Zhang,Tiancheng Wang,David Doermann
視頻videohttp://file.papertrans.cn/664/663727/663727.mp4
概述Review recent advances in CNN compression and acceleration.Elaborate recent advances on deep model compression technologies.Introduce applications of model compression in image classification, speech
叢書名稱Computational Intelligence Methods and Applications
圖書封面Titlebook: Neural Networks with Model Compression;  Baochang Zhang,Tiancheng Wang,David Doermann Book 2024 The Editor(s) (if applicable) and The Autho
描述.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 rec
出版日期Book 2024
關鍵詞Binary Neural Network; Model Compression; Artificial Intelligence; Machine Learning; Computer Vision
版次1
doihttps://doi.org/10.1007/978-981-99-5068-3
isbn_softcover978-981-99-5070-6
isbn_ebook978-981-99-5068-3Series ISSN 2510-1765 Series E-ISSN 2510-1773
issn_series 2510-1765
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:37:33 | 只看該作者
Quantization of Neural Networks,presentation and quantization have been long-standing in digital computing, NNs offer unique opportunities for advancements in this area. Although this survey primarily focuses on quantization for inference, it is important to acknowledge that quantization has also shown promise in NN training.
板凳
發(fā)表于 2025-3-22 03:01:28 | 只看該作者
Applications,us real tasks with the help of these binary methods, including image classification, image classification, speech recognition, and object detection and tracking. In this section, we introduce the applications of binary neural networks in these fields.
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https://doi.org/10.1007/978-981-99-5068-3Binary Neural Network; Model Compression; Artificial Intelligence; Machine Learning; Computer Vision
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發(fā)表于 2025-3-23 08:46:04 | 只看該作者
Binary Neural Networks,This chapter provides an overview of the most recent developments in binary neural network (BNN) technologies, with a particular focus on their suitability for front-end, edge-based computing.
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