یادگیری عمیق از طریق مدل‌سازی پراکنده و کم‌رتبه (بینایی کامپیوتری و شناسایی الگو)

__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. Cover Computer Vision andPattern Recognition Series DEEP LEARNING THROUGH SPARSE AND LOW-RANK MODELING Copyright 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 Classification Example 2.1 Introduction 2.2 Formulation and Algorithm 2.2.1 Notations 2.2.2 Joint Feature Extraction and Classification 2.2.2.1 Sparse Coding for Feature Extraction 2.2.2.2 Task-Driven Functions for Classification 2.2.2.3 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 Classification Performance on AVIRIS Indiana Pines Data 2.3.2 Classification Performance on AVIRIS Salinas Data 2.3.3 Classification 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 3.2.2 Network Implementation of l1-Approximation 3.3 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 Classification 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 4.1.2 Related Work 4.1.3 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 4.1.7 Experiments 4.1.7.1 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 5 From Bi-Level Sparse Clustering to Deep Clustering 5.1 A Joint Optimization Framework of Sparse Coding and Discriminative Clustering 5.1.1 Introduction 5.1.2 Model Formulation 5.1.2.1 Sparse Coding with Graph Regularization 5.1.2.2 Bi-level Optimization Formulation 5.1.3 Clustering-Oriented Cost Functions 5.1.3.1 Entropy-Minimization Loss 5.1.3.2 Maximum-Margin Loss 5.1.4 Experiments 5.1.4.1 Datasets 5.1.4.2 Evaluation Metrics 5.1.4.3 Comparison Experiments Comparison Methods Comparison Analysis Varying the Number of Clusters Initialization and Parameters 5.1.5 Conclusion 5.1.6 Appendix 5.2 Learning a Task-Specific Deep Architecture for Clustering 5.2.1 Introduction 5.2.2 Related Work 5.2.2.1 Sparse Coding for Clustering 5.2.2.2 Deep Learning for Clustering 5.2.3 Model Formulation 5.2.3.1 TAGnet: Task-specific And Graph-regularized Network 5.2.3.2 Clustering-Oriented Loss Functions 5.2.3.3 Connections to Existing Models 5.2.4 A Deeper Look: Hierarchical Clustering by DTAGnet 5.2.5 Experiment Results 5.2.5.1 Datasets and Measurements 5.2.5.2 Experiment Settings 5.2.5.3 Comparison Experiments and Analysis Benefits of the Task-specific Deep Architecture Effects of Graph Regularization Scalability and Robustness 5.2.5.4 Hierarchical Clustering on CMU MultiPIE 5.2.6 Conclusion References 6 Signal Processing 6.1 Deeply Optimized Compressive Sensing 6.1.1 Background 6.1.2 An End-to-End Optimization Model of CS 6.1.3 DOCS: Feed-Forward and Jointly Optimized CS Complexity Related Work 6.1.4 Experiments Settings Simulation Reconstruction Error Efficiency Experiments on Image Reconstruction 6.1.5 Conclusion 6.2 Deep Learning for Speech Denoising 6.2.1 Introduction 6.2.2 Neural Networks for Spectral Denoising 6.2.2.1 Network Architecture 6.2.2.2 Implementation Details Activation Function Cost Function Training Strategy 6.2.2.3 Extracting Denoised Signals 6.2.2.4 Dealing with Gain 6.2.3 Experimental Results 6.2.3.1 Experimental Setup 6.2.3.2 Network Structure Analysis 6.2.3.3 Analysis of Robustness to Variations 6.2.3.4 Comparison with NMF 6.2.4 Conclusion and Future Work References 7 Dimensionality Reduction 7.1 Marginalized Denoising Dictionary Learning with Locality Constraint 7.1.1 Introduction 7.1.2 Related Works 7.1.2.1 Dictionary Learning 7.1.2.2 Auto-encoder 7.1.3 Marginalized Denoising Dictionary Learning with Locality Constraint 7.1.3.1 Preliminaries and Motivations 7.1.3.2 LC-LRD Revisited 7.1.3.3 Marginalized Denoising Auto-encoder (mDA) 7.1.3.4 Proposed MDDL Model 7.1.3.5 Optimization 7.1.3.6 Classification Based on MDDL 7.1.4 Experiments 7.1.4.1 Experimental Settings 7.1.4.2 Face Recognition 7.1.4.3 Object Recognition 7.1.4.4 Digits Recognition 7.1.5 Conclusion 7.1.6 Future Works 7.2 Learning a Deep l∞ Encoder for Hashing 7.2.1 Introduction 7.2.1.1 Problem Definition and Background 7.2.1.2 Related Work 7.2.2 ADMM Algorithm 7.2.3 Deep l∞ Encoder 7.2.4 Deep l∞ Siamese Network for Hashing 7.2.5 Experiments in Image Hashing 7.2.6 Conclusion References 8 Action Recognition 8.1 Deeply Learned View-Invariant Features for Cross-View Action Recognition 8.1.1 Introduction 8.1.2 Related Work 8.1.3 Deeply Learned View-Invariant Features 8.1.3.1 Sample-Affinity Matrix (SAM) 8.1.3.2 Preliminaries on Autoencoders 8.1.3.3 Single-Layer Feature Learning 8.1.3.4 Learning 8.1.3.5 Deep Architecture 8.1.4 Experiments 8.1.4.1 IXMAS Dataset One-to-One Cross-view Action Recognition Many-to-One Cross-view Action Recognition 8.1.4.2 Daily and Sports Activities Data Set Many-to-One Cross-view Action Classification 8.2 Hybrid Neural Network for Action Recognition from Depth Cameras 8.2.1 Introduction 8.2.2 Related Work 8.2.3 Hybrid Convolutional-Recursive Neural Networks 8.2.3.1 Architecture Overview 8.2.3.2 3D Convolutional Neural Networks 8.2.3.3 3D Recursive Neural Networks 8.2.3.4 Multiple 3D-RNNs 8.2.3.5 Model Learning 8.2.3.6 Classification 8.2.4 Experiments 8.2.4.1 MSR-Gesture3D Dataset 8.2.4.2 MSR-Action3D Dataset 8.3 Summary References 9 Style Recognition and Kinship Understanding 9.1 Style Classification by Deep Learning 9.1.1 Background 9.1.2 Preliminary Knowledge of Stacked Autoencoder (SAE) 9.1.3 Style Centralizing Autoencoder 9.1.3.1 One Layer Basic SCAE 9.1.3.2 Stacked SCAE (SSCAE) 9.1.3.3 Visualization of Encoded Feature in SCAE 9.1.3.4 Geometric Interpretation of SCAE 9.1.4 Consensus Style Centralizing Autoencoder 9.1.4.1 Low-Rank Constraint on the Model 9.1.4.2 Group Sparsity Constraint on the Model 9.1.4.3 Rank-Constrained Group Sparsity Autoencoder 9.1.4.4 Efficient Solutions for RCGSAE 9.1.4.5 Progressive CSCAE 9.1.5 Experiments 9.1.5.1 Dataset 9.1.5.2 Compared Methods 9.1.5.3 Experimental Results 9.2 Visual Kinship Understanding 9.2.1 Background 9.2.2 Related Work 9.2.3 Family Faces 9.2.4 Regularized Parallel Autoencoders 9.2.4.1 Problem Formulation 9.2.4.2 Low-Rank Reframing 9.2.4.3 Solution 9.2.5 Experimental Results 9.2.5.1 Kinship Verification 9.2.5.2 Family Membership Recognition 9.3 Research Challenges and Future Works References 10 Image Dehazing: Improved Techniques 10.1 Introduction 10.2 Review and Task Description 10.2.1 Haze Modeling and Dehazing Approaches 10.2.2 RESIDE Dataset 10.3 Task 1: Dehazing as Restoration 10.4 Task 2: Dehazing for Detection 10.4.1 Solution Set 1: Enhancing Dehazing and/or Detection Modules in the Cascade 10.4.2 Solution Set 2: Domain-Adaptive Mask-RCNN Experiments 10.5 Conclusion References 11 Biomedical Image Analytics: Automated Lung Cancer Diagnosis 11.1 Introduction 11.2 Related Work 11.3 Methodology Metrics for Scoring Images 11.4 Experiments 11.5 Conclusion Acknowledgments References Index Back Cover 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. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications