Supervised dictionary learning

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Table of contents

I Introduction 
A Context
B Learning paradigms
C Motivations
D Manuscript outlines
II Methodological pillars 
A Dictionary learning
A.1 Sparse coding
A.2 Dictionary update
A.3 Conclusion on dictionary learning
B Supervised dictionary learning
B.1 SDL with internal classifier
B.2 SDL with atoms discriminative
B.3 Conclusion about SDL
C Deep Learning
C.1 Neural networks
C.2 Convolutional neural networks
C.3 Standard CNNs
C.4 Optimizers
D Manifold learning
IIISemi-supervised dictionary learning 
A Introduction
A.1 Generalities
A.2 Related works
B Proposed method
B.1 Construction of objective function
B.2 Optimization
B.3 Numerical experiments
B.4 Conclusion about proposed method
C Conclusion
IV Semi-supervised deep learning 
A Related works
A.1 Notations
A.2 Auxiliary task as regularization
A.3 Pseudo labeling
A.4 Generative models
A.5 Virtual Adversarial Training
A.6 Holistic methods
A.7 Partial conclusion for semi-supervised neural networks
B Manifold attack
B.1 Individual attack point versus data points
B.2 Attack points as data augmentation
B.3 Pairwise manifold learning
B.4 Settings of anchor points and initialization of virtual points
C Applications of manifold attack
C.1 Manifold learning on a small dataset
C.2 Robustness to adversarial examples
C.3 Semi-supervised manifold attack
C.4 Conclusion about manifold attack
V General Conclusion