| Description |
1 online resource (223 p.) |
| Contents |
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 |
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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 |
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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) |
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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 |
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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 |
| Notes |
Description based upon print version of record |
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4.4.3 LS-GAN |
| Form |
Electronic book
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| Author |
D Pathak, Pranav
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R Sakhare, Sachin
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Patil, Sonali
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| ISBN |
9781000840568 |
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1000840565 |
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