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Deep learning through sparse and low...

Wang, Zhangyang,

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Deep learning through sparse and low-rank modeling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning through sparse and low-rank modeling/ edited by Zhangyang Wang, Yun Fu, Thomas S. Huang.
其他作者:	Wang, Zhangyang,
出版者:	London, United Kingdom :Academic Press, an imprint of Elsevier, : 2019.,
面頁冊數:	1 online resource.
內容註:	Front Cover; Deep Learning Through Sparse and Low-Rank Modeling; Copyright; Contents; Contributors; About the Editors; Preface; Acknowledgments; 1 Introduction; 1.1 Basics of Deep Learning; 1.2 Basics of Sparsity and Low-Rankness; 1.3 Connecting Deep Learning to Sparsity and Low-Rankness; 1.4 Organization; References; 2 Bi-Level Sparse Coding: A Hyperspectral Image Classi cation Example; 2.1 Introduction; 2.2 Formulation and Algorithm; 2.2.1 Notations; 2.2.2 Joint Feature Extraction and Classi cation; 2.2.2.1 Sparse Coding for Feature Extraction
內容註:	2.2.2.2 Task-Driven Functions for Classi cation2.2.2.3 Spatial Laplacian Regularization; 2.2.3 Bi-level Optimization Formulation; 2.2.4 Algorithm; 2.2.4.1 Stochastic Gradient Descent; 2.2.4.2 Sparse Reconstruction; 2.3 Experiments; 2.3.1 Classi cation Performance on AVIRIS Indiana Pines Data; 2.3.2 Classi cation Performance on AVIRIS Salinas Data; 2.3.3 Classi cation Performance on University of Pavia Data; 2.4 Conclusion; 2.5 Appendix; References; 3 Deep l0 Encoders: A Model Unfolding Example; 3.1 Introduction; 3.2 Related Work; 3.2.1 l0- and l1-Based Sparse Approximations
內容註:	3.2.2 Network Implementation of l1-Approximation3.3 Deep l0 Encoders; 3.3.1 Deep l0-Regularized Encoder; 3.3.2 Deep M-Sparse l0 Encoder; 3.3.3 Theoretical Properties; 3.4 Task-Driven Optimization; 3.5 Experiment; 3.5.1 Implementation; 3.5.2 Simulation on l0 Sparse Approximation; 3.5.3 Applications on Classi cation; 3.5.4 Applications on Clustering; 3.6 Conclusions and Discussions on Theoretical Properties; References; 4 Single Image Super-Resolution: From Sparse Coding to Deep Learning; 4.1 Robust Single Image Super-Resolution via Deep Networks with Sparse Prior; 4.1.1 Introduction
內容註:	4.1.2 Related Work4.1.3 Sparse Coding Based Network for Image SR; 4.1.3.1 Image SR Using Sparse Coding; 4.1.3.2 Network Implementation of Sparse Coding; 4.1.3.3 Network Architecture of SCN; 4.1.3.4 Advantages over Previous Models; 4.1.4 Network Cascade for Scalable SR; 4.1.4.1 Network Cascade for SR of a Fixed Scaling Factor; 4.1.4.2 Network Cascade for Scalable SR; 4.1.4.3 Training Cascade of Networks; 4.1.5 Robust SR for Real Scenarios; 4.1.5.1 Data-Driven SR by Fine-Tuning; 4.1.5.2 Iterative SR with Regularization; Blurry Image Upscaling; Noisy Image Upscaling; 4.1.6 Implementation Details
內容註:	4.1.7 Experiments4.1.7.1 Algorithm Analysis; 4.1.7.2 Comparison with State-of-the-Art; 4.1.7.3 Robustness to Real SR Scenarios; Data-Driven SR by Fine-Tuning; Regularized Iterative SR; 4.1.8 Subjective Evaluation; 4.1.9 Conclusion and Future Work; 4.2 Learning a Mixture of Deep Networks for Single Image Super-Resolution; 4.2.1 Introduction; 4.2.2 The Proposed Method; 4.2.3 Implementation Details; 4.2.4 Experimental Results; 4.2.4.1 Network Architecture Analysis; 4.2.4.2 Comparison with State-of-the-Art; 4.2.4.3 Runtime Analysis; 4.2.5 Conclusion and Future Work; References
標題:	Machine learning. -
電子資源:	https://www.sciencedirect.com/science/book/9780128136591
ISBN:	9780128136607 (electronic bk.)

Deep learning through sparse and low-rank modeling
Deep learning through sparse and low-rank modeling[electronic resource] /edited by Zhangyang Wang, Yun Fu, Thomas S. Huang. - London, United Kingdom :Academic Press, an imprint of Elsevier,2019. - 1 online resource. - Computer vision and pattern recognition series. - Computer vision and pattern recognition series..

Includes bibliographical references and index.

Front Cover; Deep Learning Through Sparse and Low-Rank Modeling; Copyright; Contents; Contributors; About the Editors; Preface; Acknowledgments; 1 Introduction; 1.1 Basics of Deep Learning; 1.2 Basics of Sparsity and Low-Rankness; 1.3 Connecting Deep Learning to Sparsity and Low-Rankness; 1.4 Organization; References; 2 Bi-Level Sparse Coding: A Hyperspectral Image Classi cation Example; 2.1 Introduction; 2.2 Formulation and Algorithm; 2.2.1 Notations; 2.2.2 Joint Feature Extraction and Classi cation; 2.2.2.1 Sparse Coding for Feature Extraction

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.

ISBN: 9780128136607 (electronic bk.)Subjects--Topical Terms:

533906
Machine learning.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Deep learning through sparse and low-rank modeling
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505 0 $a Front Cover; Deep Learning Through Sparse and Low-Rank Modeling; Copyright; Contents; Contributors; About the Editors; Preface; Acknowledgments; 1 Introduction; 1.1 Basics of Deep Learning; 1.2 Basics of Sparsity and Low-Rankness; 1.3 Connecting Deep Learning to Sparsity and Low-Rankness; 1.4 Organization; References; 2 Bi-Level Sparse Coding: A Hyperspectral Image Classi cation Example; 2.1 Introduction; 2.2 Formulation and Algorithm; 2.2.1 Notations; 2.2.2 Joint Feature Extraction and Classi cation; 2.2.2.1 Sparse Coding for Feature Extraction
505 8 $a 2.2.2.2 Task-Driven Functions for Classi cation2.2.2.3 Spatial Laplacian Regularization; 2.2.3 Bi-level Optimization Formulation; 2.2.4 Algorithm; 2.2.4.1 Stochastic Gradient Descent; 2.2.4.2 Sparse Reconstruction; 2.3 Experiments; 2.3.1 Classi cation Performance on AVIRIS Indiana Pines Data; 2.3.2 Classi cation Performance on AVIRIS Salinas Data; 2.3.3 Classi cation Performance on University of Pavia Data; 2.4 Conclusion; 2.5 Appendix; References; 3 Deep l0 Encoders: A Model Unfolding Example; 3.1 Introduction; 3.2 Related Work; 3.2.1 l0- and l1-Based Sparse Approximations
505 8 $a 3.2.2 Network Implementation of l1-Approximation3.3 Deep l0 Encoders; 3.3.1 Deep l0-Regularized Encoder; 3.3.2 Deep M-Sparse l0 Encoder; 3.3.3 Theoretical Properties; 3.4 Task-Driven Optimization; 3.5 Experiment; 3.5.1 Implementation; 3.5.2 Simulation on l0 Sparse Approximation; 3.5.3 Applications on Classi cation; 3.5.4 Applications on Clustering; 3.6 Conclusions and Discussions on Theoretical Properties; References; 4 Single Image Super-Resolution: From Sparse Coding to Deep Learning; 4.1 Robust Single Image Super-Resolution via Deep Networks with Sparse Prior; 4.1.1 Introduction
505 8 $a 4.1.2 Related Work4.1.3 Sparse Coding Based Network for Image SR; 4.1.3.1 Image SR Using Sparse Coding; 4.1.3.2 Network Implementation of Sparse Coding; 4.1.3.3 Network Architecture of SCN; 4.1.3.4 Advantages over Previous Models; 4.1.4 Network Cascade for Scalable SR; 4.1.4.1 Network Cascade for SR of a Fixed Scaling Factor; 4.1.4.2 Network Cascade for Scalable SR; 4.1.4.3 Training Cascade of Networks; 4.1.5 Robust SR for Real Scenarios; 4.1.5.1 Data-Driven SR by Fine-Tuning; 4.1.5.2 Iterative SR with Regularization; Blurry Image Upscaling; Noisy Image Upscaling; 4.1.6 Implementation Details
505 8 $a 4.1.7 Experiments4.1.7.1 Algorithm Analysis; 4.1.7.2 Comparison with State-of-the-Art; 4.1.7.3 Robustness to Real SR Scenarios; Data-Driven SR by Fine-Tuning; Regularized Iterative SR; 4.1.8 Subjective Evaluation; 4.1.9 Conclusion and Future Work; 4.2 Learning a Mixture of Deep Networks for Single Image Super-Resolution; 4.2.1 Introduction; 4.2.2 The Proposed Method; 4.2.3 Implementation Details; 4.2.4 Experimental Results; 4.2.4.1 Network Architecture Analysis; 4.2.4.2 Comparison with State-of-the-Art; 4.2.4.3 Runtime Analysis; 4.2.5 Conclusion and Future Work; References
520 $a Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
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