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Deep learning through sparse and low-rank modeling /
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 tool...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Academic Press, an imprint of Elsevier,
[2019]
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| Colección: | Computer vision and pattern recognition series.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 0 | 0 | |a Deep learning through sparse and low-rank modeling / |c edited by Zhangyang Wang, Yun Fu, Thomas S. Huang. |
| 264 | 1 | |a [Place of publication not identified] : |b Academic Press, an imprint of Elsevier, |c [2019] | |
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| 490 | 1 | |a Computer vision and pattern recognition series | |
| 504 | |a Includes bibliographical references and index. | ||
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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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
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| 700 | 1 | |a Wang, Zhangyang, |e editor. | |
| 700 | 1 | |a Fu, Yun, |e editor. | |
| 700 | 1 | |a Huang, Thomas S., |d 1936- |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Deep learning through sparse and low-rank modeling. |d [Place of publication not identified] : Academic Press, an imprint of Elsevier, [2019] |z 0128136596 |z 9780128136591 |w (OCoLC)1022780543 |
| 830 | 0 | |a Computer vision and pattern recognition series. | |
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