Generative adversarial networks and deep learning : theory and applications /
edited by edited by Roshani Raut, Pranav D. Pathak, Sachin R. Sakhare, Sonali Patil.
Bok Engelsk 2023
| Medvirkende | Raut, Roshani, (editor.)
|
|---|---|
| Omfang | xiv, 208 pages;
|
| Opplysninger | 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.. - 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.. - 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).. - "This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A generative adversarial network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation, text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc"--
|
| Emner | |
| ISBN | 1-00-320396-5. - 1-000-84055-7. - 1-000-84056-5. - 1-003-20396-5
|
