Generative Adversarial Networks and Deep Learning: Theory and Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
List of Contributors
1 Generative Adversarial Networks and Its Use Cases
	1.1 Introduction
	1.2 Supervised Learning
		1.2.1 Unsupervised Learning
	1.3 Background of GAN
		1.3.1 Image-To-Image Translation
	1.4 Difference Between Auto Encoders and Generative Adversarial Networks
		1.4.1 Auto Encoders
		1.4.2 Generative Adversarial Networks
	1.5 Difference Between VAN and Generative Adversarial Networks
	1.6 Application of GANs
		1.6.1 Application of GANs in Healthcare
		1.6.2 Applications of Generative Models
			1.6.2.1 Generate Examples for Image Datasets
			1.6.2.2 Generate Realistic Photographs
			1.6.2.3 Generate Cartoon Characters
			1.6.2.4 Image-To-Image Translation
			1.6.2.5 Text-To-Image Translation
			1.6.2.6 Semantic-Image-To-Photo Translation
			1.6.2.7 Photos to Emojis
			1.6.2.8 Photograph Editing
			1.6.2.9 Face Aging
	1.7 Conclusion
	References
2 Image-To-Image Translation Using Generative Adversarial Networks
	2.1 Introduction
	2.2 Conventional I2I Translations
		2.2.1 Filtering-Based I2I
		2.2.2 Optimisation-Based I2I
		2.2.3 Dictionary Learning-Based I2I
		2.2.4 Deep Learning-Based I2I
		2.2.5 GAN-Based I2I
	2.3 Generative Adversarial Networks (GAN)
		2.3.1 How GANs Work
		2.3.2 Loss Functions
			2.3.2.1 Minimax Loss
		2.3.3 Other Generative Models
	2.4 Supervised I2I Translation
		2.4.1 Pix2Pix
			2.4.1.1 Applications of Pix2Pix Models
		2.4.2 Additional Work On Supervised I2I Translations
			2.4.2.1 Single-Modal Outputs
			2.4.2.2 Multimodal Outputs
	2.5 Unsupervised I2I (UI2I) Translation
		2.5.1 Deep Convolutional GAN (DCGAN)
			2.5.1.1 DCGAN Applications
		2.5.2 Conditional GAN (CGAN)
		2.5.3 Cycle GAN
			2.5.3.1 Cycle Consistency Loss
			2.5.3.2 CycleGAN Applications
		2.5.4 Additional Work On Unsupervised I2I
			2.5.4.1 Single-Modal Outputs
	2.6 Semi-Supervised I2I
	2.7 Few-Shot I2I
	2.8 Comparative Analysis
		2.8.1 Metrics
		2.8.2 Results
	2.9 Conclusion
	References
3 Image Editing Using Generative Adversarial Network
	3.1 Introduction
	3.2 Background of GAN
	3.3 Image-To-Image Translation
	3.4 Motivation and Contribution
	3.5 GAN Objective Functions
		3.5.1 GAN Loss Challenges
		3.5.2 The Problem of GAN Loss
		3.5.3 Loss of Discriminator
		3.5.4 GAN Loss Minimax
	3.6 Image-To-Image Translation
		3.6.1 Controlled Image-To-Image Conversion
			3.6.1.1 CGAN
			3.6.1.2 BicycleGAN
			3.6.1.3 SPA-GAN
			3.6.1.4 CE-GAN
		3.6.2 Unsupervised Image to Image Conversion
			3.6.2.1 CycleGAN
			3.6.2.2 Dugan
			3.6.2.3 UNIT
			3.6.2.4 MUNIT
	3.7 Application
	3.8 Conclusion
	References
4 Generative Adversarial Networks for Video-To-Video Translation
	4.1 Introduction
	4.2 Description of Background
		4.2.1 Objectives
	4.3 Different Methods and Architectures
	4.4 Architecture
		4.4.1 Cycle GAN
		4.4.2 Style GAN
		4.4.3 LS-GAN
		4.4.4 Disco GAN
		4.4.5 Mo-Cycle GAN
		4.4.6 Different GANs for Video Synthesis (Fixed Length)
		4.4.7 TGAN
		4.4.8 Generative Adversarial Network: Flexible Dimension Audiovisual Combination
			4.4.8.1 MoCo GAN
			4.4.8.2 DVD GAN
			4.4.8.3 Methods and Tools for GAN
			4.4.8.4 GAN Lab
		4.4.9 Hyper GAN
		4.4.10 Imaginaire
		4.4.11 GAN Tool Compartment
		4.4.12 Mimicry
		4.4.13 Pygan
		4.4.14 Studio GAN
		4.4.15 Torch GAN
		4.4.16 TF-GAN
		4.4.17 Ve GANs
	4.5 Conclusions
	References
5 Security Issues in Generative Adversarial Networks
	5.1 Introduction
	5.2 Motivation
		5.2.1 Objectives
	5.3 Related Work
		5.3.1 Generative Adversarial Network
		5.3.2 Overview of Security
		5.3.3 GANs in Safety
			5.3.3.1 Obscuring Delicate Information
		5.3.4 Cyber Interruption and Malware Detection
		5.3.5 Security Examination
	5.4 Security Attacks in GANs
		5.4.1 Cracking Passphrases
		5.4.2 Hiding Malware
		5.4.3 Forging Facial Detection
		5.4.4 Detection and Response
	5.5 Conclusion
	References
6 Generative Adversarial Networks-Aided Intrusion Detection System
	6.1 Introduction
	6.2 Application of GANs for Resolving Data Imbalance
	6.3 Application of GAN as a Deep Learning Classifier
	6.4 Application of GANs for Generating Adversarial Examples
	6.5 Conclusion
	Glossary of Terms, Acronyms and Abbreviations
	References
7 Textual Description to Facial Image Generation
	7.1 Introduction
	7.2 Literature Review
	7.3 Dataset Description
	7.4 Proposed Methodology
		7.4.1 Generator
			7.4.1.1 DAN (Deep Averaging Network)
			7.4.1.2 Transformer Encoder
		7.4.2 Discriminator
		7.4.3 Training of GAN
			7.4.3.1 Loss Function
			7.4.3.2 Optimizer
			7.4.3.3 Discriminative Learning Rates
			7.4.3.4 Dropout
	7.5 Limitations
	7.6 Future Scope
	7.7 Conclusion
	7.8 Applications
	References
8 An Application of Generative Adversarial Network in Natural Language Generation
	8.1 Introduction
	8.2 Generative Adversarial Network Model
		8.2.1 Working of Generative Adversarial Network
		8.2.2 Natural Language Generation
	8.3 Background and Motivation
	8.4 Related Work
	8.5 Issues and Challenges
	8.6 Case Studies: Application of Generative Adversarial Network
		8.6.1 Creating Machines to Paint, Write, Compose, and Play
		8.6.2 Use of GAN in Text Generation
		8.6.3 Indian Sign Language Generation Using Sentence Processing and Generative Adversarial Networks
		8.6.4 Applications of GAN in Natural Language Processing
	8.7 Conclusions
	References
9 Beyond Image Synthesis: GAN and Audio
	9.1 Introduction
		9.1.1 Audio Signals
	9.2 About GANs
	9.3 Working Principal of GANs
	9.4 Literatutre Survey About Different GANs
		9.4.1 Time Sequence Gan Adversarial Network
		9.4.2 Vector-Quantized Contrastive Predictive Coding-GAN
		9.4.3 The VQCPC Encoder
		9.4.4 The Generative Adversarial Network Designs
	9.5 Results
		9.5.1 Dataset
		9.5.2 Assessment
	9.6 Baselines
	9.7 Quantitative Outcomes
	9.8 Casual Tuning In
	9.9 Results
	References
10 A Study On the Application Domains of Electroencephalogram for the Deep Learning-Based Transformative Healthcare
	10.1 Introduction
	10.2 Modalities of Deep Learning-Based Healthcare Applications
		10.2.1 Medical Image Generation and Synthesis
		10.2.2 EEG Signal Reconstruction and SSVEP Classification
		10.2.3 Body Sensor-Induced Healthcare Applications
	10.3 Healthcare Application Areas of EEG
		10.3.1 Rare Disease Diagnosis
		10.3.2 Robotics-Based Applications of Deep Learning Inducing EEG
		10.3.3 Rehabilitation
			10.3.3.1 Bipolar Disorder
			10.3.3.2 Drug Rehabilitation
			10.3.3.3 Gait Rehabilitation
			10.3.3.4 Vascular Hemiplegia Rehabilitation
			10.3.3.5 Dementia
			10.3.3.6 Epilepsy
	10.4 Significance of Different Electrode Placement Techniques
		10.4.1 10–20 International System
		10.4.2 10–10 System
		10.4.3 10–5 System
	10.5 Conclusion
	References
11 Emotion Detection Using Generative Adversarial Network
	11.1 Introduction
	11.2 Background Study
	11.3 Deep Learning Methods Used in Gaming Applications
		11.3.1 Super-Resolution GAN
		11.3.2 Deep Convolutional Generative Adversarial Network (DC-GAN)
		11.3.3 Conditional Embedding Self-Attention Generative Adversarial Network
		11.3.4 Variational Autoencoders Generative Adversarial Network
		11.3.5 Conditional Generative Adversarial Network (CGAN)
		11.3.6 Token-Based One-Shot Arbitrary Dimension Generative Adversarial Network
		11.3.7 Poker-Face Generative Adversarial Network
	11.4 Application Areas
		11.4.1 Quality Enhancement
		11.4.2 Differential Rendering
		11.4.3 Character Auto-Creation and Customization
		11.4.4 Procedural Content Generation
		11.4.5 Video Game Evaluation
		11.4.6 User Emotion Identification
	11.5 Conclusion
	References
12 Underwater Image Enhancement Using Generative Adversarial Network
	12.1 Introduction
	12.2 Literature Review
	12.3 Proposed Method
		12.3.1 Loss Function
		12.3.2 Discriminator Loss
		12.3.3 Generator Loss
	12.4 Generative Adversarial Networks
	12.5 Atrous Convolution
	12.6 Experimental Results
		12.6.1 Underwater Image Quality Measure (UIQM)
	12.7 Conclusions
	References
13 Towards GAN Challenges and Its Optimal Solutions
	13.1 Introduction: Background and Driving Forces
	13.2 Challenges With GAN
	13.3 GAN Training Problems
		13.3.1 NashEquilibrium
		13.3.2 Vanishing Gradient
			13.3.2.1 Mode Collapse and Non-Convergence
	13.4 Conclusion
	References
Index