Table of Contents
1.2. Basics of Sparsity and Low-Rankness
1.3. Connecting Deep Learning to Sparsity and Low-Rankness
Chapter 2: Bi-Level Sparse Coding: A Hyperspectral Image Classification Example
2.2. Formulation and Algorithm
Chapter 3: Deep ℓ0 Encoders: A Model Unfolding Example
3.6. Conclusions and Discussions on Theoretical Properties
Chapter 4: Single Image Super-Resolution: From Sparse Coding to Deep Learning
4.1. Robust Single Image Super-Resolution via Deep Networks with Sparse Prior
4.2. Learning a Mixture of Deep Networks for Single Image Super-Resolution
Chapter 5: From Bi-Level Sparse Clustering to Deep Clustering
5.1. A Joint Optimization Framework of Sparse Coding and Discriminative Clustering
5.2. Learning a Task-Specific Deep Architecture for Clustering
6.1. Deeply Optimized Compressive Sensing
6.2. Deep Learning for Speech Denoising
Chapter 7: Dimensionality Reduction
7.1. Marginalized Denoising Dictionary Learning with Locality Constraint
7.2. Learning a Deep ℓ∞ Encoder for Hashing
8.1. Deeply Learned View-Invariant Features for Cross-View Action Recognition
8.2. Hybrid Neural Network for Action Recognition from Depth Cameras
Chapter 9: Style Recognition and Kinship Understanding
9.1. Style Classification by Deep Learning
9.2. Visual Kinship Understanding
9.3. Research Challenges and Future Works
Chapter 10: Image Dehazing: Improved Techniques
10.2. Review and Task Description
10.3. Task 1: Dehazing as Restoration
10.4. Task 2: Dehazing for Detection
Chapter 11: Biomedical Image Analytics: Automated Lung Cancer Diagnosis