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by Thomas S. Huang, Yun Fu, Zhangyang Wang
Deep Learning through Sparse and Low-Rank Modeling
Cover image
Title page
Table of Contents
Copyright
Contributors
About the Editors
Preface
Acknowledgments
Chapter 1: Introduction
Abstract
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
Chapter 2: Bi-Level Sparse Coding: A Hyperspectral Image Classification Example
Abstract
2.1. Introduction
2.2. Formulation and Algorithm
2.3. Experiments
2.4. Conclusion
2.5. Appendix
References
Chapter 3: Deep ℓ0 Encoders: A Model Unfolding Example
Abstract
3.1. Introduction
3.2. Related Work
3.3. Deep ℓ0 Encoders
3.4. Task-Driven Optimization
3.5. Experiment
3.6. Conclusions and Discussions on Theoretical Properties
References
Chapter 4: Single Image Super-Resolution: From Sparse Coding to Deep Learning
Abstract
4.1. Robust Single Image Super-Resolution via Deep Networks with Sparse Prior
4.2. Learning a Mixture of Deep Networks for Single Image Super-Resolution
References
Chapter 5: From Bi-Level Sparse Clustering to Deep Clustering
Abstract
5.1. A Joint Optimization Framework of Sparse Coding and Discriminative Clustering
5.2. Learning a Task-Specific Deep Architecture for Clustering
References
Chapter 6: Signal Processing
Abstract
6.1. Deeply Optimized Compressive Sensing
6.2. Deep Learning for Speech Denoising
References
Chapter 7: Dimensionality Reduction
Abstract
7.1. Marginalized Denoising Dictionary Learning with Locality Constraint
7.2. Learning a Deep ℓ∞ Encoder for Hashing
References
Chapter 8: Action Recognition
Abstract
8.1. Deeply Learned View-Invariant Features for Cross-View Action Recognition
8.2. Hybrid Neural Network for Action Recognition from Depth Cameras
8.3. Summary
References
Chapter 9: Style Recognition and Kinship Understanding
Abstract
9.1. Style Classification by Deep Learning
9.2. Visual Kinship Understanding
9.3. Research Challenges and Future Works
References
Chapter 10: Image Dehazing: Improved Techniques
Abstract
10.1. Introduction
10.2. Review and Task Description
10.3. Task 1: Dehazing as Restoration
10.4. Task 2: Dehazing for Detection
10.5. Conclusion
References
Chapter 11: Biomedical Image Analytics: Automated Lung Cancer Diagnosis
Abstract
Acknowledgements
11.1. Introduction
11.2. Related Work
11.3. Methodology
11.4. Experiments
11.5. Conclusion
References
Index
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