Generative Adversarial Learning: Architectures and Applications

Preface
Contents
1 An Introduction to Generative Adversarial Learning: Architectures and Applications
	1.1 Book Outline
	References
2 Generative Adversarial Networks:  A Survey on Training, Variants, and Applications
	2.1 Introduction
	2.2 GAN Variants
		2.2.1 Original Generative Adversarial Network (GAN)
		2.2.2 Deep Convolutional Generative Adversarial Network (DCGAN)
		2.2.3 Conditional Generative Adversarial Network (CGAN)
		2.2.4 Information Maximizing Generative Adversarial Networks (InfoGAN)
		2.2.5 Auxiliary Classifier Generative Adversarial Network (ACGAN)
		2.2.6 Stacked Generative Adversarial Networks (StackGAN)
		2.2.7 Cycle-Consistent Generative Adversarial Network (CycleGAN)
		2.2.8 Wasserstein Generative Adversarial Network (WGAN)
		2.2.9 Semi-Supervised Generative Adversarial Network (SSGAN)
		2.2.10 Progressive Growing Generative Adversarial Network (Progressive GAN)
		2.2.11 Style-Based Generative Adversarial Network (StyleGAN)
		2.2.12 Bidirectional Generative Adversarial Network (BiGAN)
		2.2.13 Bayesian Generative Adversarial Network (BGAN)
	2.3 GAN Applications
	2.4 Conclusions
	References
3 Fair Data Generation and Machine Learning Through Generative Adversarial Networks
	3.1 Introduction
	3.2 Overview of FairGAN Framework
		3.2.1 Definitions and Metrics of Fairness
		3.2.2 FairGAN Framework
	3.3 Simplified Fairness Aware Generative Adversarial Networks
		3.3.1 Model Framework Design
		3.3.2 Empirical Evaluation
	3.4 Achieving Causal Fairness in Data Generation
		3.4.1 Model Framework Design
		3.4.2 Achieving Direct/Indirect/Counterfactual Fairness
		3.4.3 Empirical Evaluation
	3.5 Achieving Fairness in Classification
		3.5.1 Model Framework Design
		3.5.2 Achieving Fair Classification
		3.5.3 Empirical Evaluation
	3.6 Related Work
		3.6.1 Dealing with Different Types of Structural Data
		3.6.2 Dealing with Privacy
	3.7 Future Directions
		3.7.1 Variants Comparison and Architecture Design
		3.7.2 Achieving Long-Term Fairness in Dynamic Decision Making
		3.7.3 Achieving Fairness in Regression
		3.7.4 Achieving Fairness in Recommendation
		3.7.5 Open Source Software
	3.8 Conclusions
	References
4 Quaternion Generative Adversarial Networks
	4.1 Introduction
	4.2 Quaternion Algebra
	4.3 Generative Learning in the Quaternion Domain
		4.3.1 The Quaternion Adversarial Framework
		4.3.2 Quaternion Fully Connected Layers
		4.3.3 Quaternion Convolutional Layers
		4.3.4 Quaternion Pooling Layers
		4.3.5 Quaternion Batch Normalization
		4.3.6 Quaternion Spectral Normalization
		4.3.7 Quaternion Weight Initialization
		4.3.8 Training
	4.4 GAN Architectures in the Quaternion Domain
		4.4.1 Vanilla QGAN
		4.4.2 Advanced QGAN
		4.4.3 Evaluation Metrics
	4.5 Experimental Evaluation
		4.5.1 Evaluation of Spectral Normalization Methods
	4.6 Conclusions
	References
5 Image Generation Using Continuous Conditional Generative Adversarial Networks
	5.1 Introduction and Motivation
	5.2 Continuous Conditional Generative Adversarial Networks
		5.2.1 Derivation of HVDL and SVDL Losses
		5.2.2 A Rule of Thumb for Hyper-parameter Selection
		5.2.3 Algorithms for Training CcGANs
	5.3 Theoretical Analysis
	5.4 Experiments
		5.4.1 Case Study 1: Circular 2-D Gaussians
		5.4.2 Case Study 2: UTKFace
	5.5 Conclusion
	References
6 Generative Adversarial Networks for Data Augmentation in Hyperspectral Image Classification
	6.1 Introduction
		6.1.1 Dataset
		6.1.2 Data Availability
		6.1.3 Class Imbalance
		6.1.4 Data Augmentation
	6.2 Previous Work
		6.2.1 Data Augmentation
		6.2.2 Class Imbalance
		6.2.3 Applications in Hyperspectral Imaging
	6.3 Wasserstein GAN
	6.4 Conditional Wasserstein Generative Adversarial Network with Gradient Penalty for Hyperspectral Image Generation
		6.4.1 Wasserstein Generative Adversarial Network with Gradient Penalty
		6.4.2 Hyperspectral Data Patch
		6.4.3 Dimensionality Reduction
		6.4.4 Discriminator and Generator Models
		6.4.5 Training Process
	6.5 Experimental Results
		6.5.1 Evaluation Metrics
		6.5.2 Experimental Setting
		6.5.3 Spectral Signature
		6.5.4 Data Augmentation
		6.5.5 Visualizations
	6.6 Conclusion and Future Work
	References
7 Face Aging Using Generative Adversarial Networks
	7.1 Introduction
	7.2 Generative Adversarial Networks
		7.2.1 Mode Collapse
		7.2.2 GANs Types
		7.2.3 Reference Architectures for Facial Imaging
	7.3 Databases Used for Facial Aging
		7.3.1 FG-NET
		7.3.2 UTKFace
		7.3.3 CACD
		7.3.4 MORPH
		7.3.5 IMDB-Wiki
		7.3.6 Cross-Age LFW (CALFW)
		7.3.7 Other Databases
	7.4 Experiments and Results
		7.4.1 CAAE Experiments
		7.4.2 IPCGAN Experiments
		7.4.3 Recursive Chaining of Reversible Image-to-Image Translators Experiments, RCRIIT
		7.4.4 Method Comparison
	7.5 Summary
	References
8 Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks
	8.1 Introduction
	8.2 Related Works
		8.2.1 Adversarially Learned Anomaly Detection
		8.2.2 GAN Ensemble for Anomaly Detection
		8.2.3 Anomaly Detection with GAN
		8.2.4 Unsupervised Change Detection with GAN
		8.2.5 Unsupervised Change Detection with GAN
	8.3 Internet of Things Network
		8.3.1 Dataset
		8.3.2 IoT Testbed
	8.4 Feature Embedding
		8.4.1 Extracting Sequential Changes
		8.4.2 Generative Adversarial Cluster Analysis
		8.4.3 Training Computational Complexity
	8.5 Experimental Results
		8.5.1 Experimental Setting
		8.5.2 Results Analysis
	8.6 Conclusion
	References
9 Inspection of Lead Frame Defects Using Deep CNN and Cycle-Consistent GAN-Based Defect Augmentation
	9.1 Introduction
	9.2 Defect Inspection Using the Faster R-CNN
		9.2.1 Materials
		9.2.2 Defect Inspection Using the Faster R-CNN
	9.3 Defect Augmentation Using the CycleGAN
		9.3.1 Basic GAN Structure
		9.3.2 CycleGAN Structure
		9.3.3 CycleGAN Training
		9.3.4 CycleGAN for Image Augmentation
	9.4 Experiments
		9.4.1 Experimental Result of CyleGAN
		9.4.2 Experimental Result of Defect Inspection Using the Faster R-CNN
	9.5 Conclusions
	References
10 Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition
	10.1 Introduction
	10.2 SHL Dataset
	10.3 State of the Art in Classifying the SHL Dataset
	10.4 Background on Generative Adversarial Networks
	10.5 Methods
		10.5.1 Feature Extraction
		10.5.2 Feature Scaling
		10.5.3 Dealing with Class Imbalance
		10.5.4 Feature Selection
		10.5.5 Proposed Generative Adversarial Network
		10.5.6 Hyperparameter Tuning
	10.6 Implementation
	10.7 Results
		10.7.1 User Specific Evaluation
		10.7.2 User Semi-independent Evaluation
		10.7.3 User Independent Evaluation
	10.8 Discussion
		10.8.1 Performance Evaluation
		10.8.2 Hyperparameter Tuning
	10.9 Conclusion
	References
11 GANs for Molecule Generation in Drug Design and Discovery
	11.1 Introduction
	11.2 Preliminary Concepts
		11.2.1 Molecular Representation
		11.2.2 Evaluation Metrics
	11.3 GAN-based Molecule Generation Models
		11.3.1 Goal-Directed Models
		11.3.2 Distribution-Learning Models
		11.3.3 Applications of GAN-based Molecule Generative Models
	11.4 Comparison with Other Generative Models for Molecule Generation
		11.4.1 Molecule Generation Based on Other Generative Models
		11.4.2 Advantages of GAN-based Models
		11.4.3 Disadvantages of GAN Based Models
	11.5 Challenges and Future Directions
		11.5.1 New Molecular Representations
		11.5.2 New Molecule Generation Models
		11.5.3 Benchmarks and Metrics
		11.5.4 New Pharmaceutical Objective Functions
		11.5.5 Influence of Property Prediction Models
	11.6 Conclusion
	References
12 Improved Diagnostic Performance of Arrhythmia Classification Using Conditional GAN Augmented Heartbeats
	12.1 Introduction
		12.1.1 ECG Synthesis Using Generative Adversarial Network
		12.1.2 Related Work on Heartbeat Classification
		12.1.3 Contributions
	12.2 Data
		12.2.1 Dataset Description
		12.2.2 Data Preprocessing
	12.3 Methodology
		12.3.1 ECG Generation Methodology
		12.3.2 Augmentation Using Deep Convolutional Conditional GANs
		12.3.3 ECG Classification Methodology
	12.4 Results and Discussion
		12.4.1 Computing Platform
		12.4.2 Evaluation Metrics
		12.4.3 DCCGAN Performance Evaluation
		12.4.4 Beat Classification Performance
	12.5 Conclusion and Future Work
	References
13 Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Perspectives
	13.1 Introduction
		13.1.1 Magnetic Resonance Imaging
		13.1.2 Limitations of Magnetic Resonance Imaging
		13.1.3 Conventional Acceleration Using Compressive Sensing
		13.1.4 Deep Learning Based Fast MRI
		13.1.5 GAN Powered Fast MRI
	13.2 Methods
		13.2.1 Fundamentals of MRI Reconstruction
		13.2.2 CNN Based MRI Reconstruction
		13.2.3 GAN Based MRI Reconstruction
		13.2.4 Evaluation Methods
	13.3 Benchmarking
	13.4 Discussion
	13.5 Conclusion
	References
14 Generative Adversarial Networks for Data Augmentation in X-Ray Medical Imaging
	14.1 Introduction
	14.2 Previous Work on Using GANs and Transfer Learning in X-Ray Imaging
	14.3 Deep Convolutional GAN (DCGAN) and Its Limitations for X-Ray Imaging
		14.3.1 Architecture of DCGAN
		14.3.2 DCGAN Training
	14.4 Progressively Growing GAN (PGGAN)
		14.4.1 Training of PGGAN
		14.4.2 Phasing in a New Layer for Smooth Model Building
		14.4.3 Transfer Learning for Model Building
	14.5 Results and Discussion
		14.5.1 Dataset
		14.5.2 Augmentation Results
	14.6 Conclusion
	References