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020 _a9789819950683
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024 7 _a10.1007/978-981-99-5068-3
_2doi
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082 0 4 _a006.31
_223
100 1 _aZhang, Baochang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_997607
245 1 0 _aNeural Networks with Model Compression
_h[electronic resource] /
_cby Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aIX, 260 p. 101 illus., 67 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _aChapter 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.
520 _aDeep 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.
650 0 _aMachine learning.
_91831
650 0 _aArtificial intelligence.
_93407
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_997610
650 1 4 _aMachine Learning.
_91831
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_997613
700 1 _aWang, Tiancheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_997615
700 1 _aXu, Sheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_997616
700 1 _aDoermann, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_997617
710 2 _aSpringerLink (Online service)
_997618
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819950676
776 0 8 _iPrinted edition:
_z9789819950690
776 0 8 _iPrinted edition:
_z9789819950706
830 0 _aComputational Intelligence Methods and Applications,
_x2510-1773
_997620
856 4 0 _uhttps://doi.org/10.1007/978-981-99-5068-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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999 _c87459
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