Generating New Reality From Autoencoders and Adversarial Networks to Deepfakes 1st Edition by Micheal Lanham ISBN 1484270916 978-1484270912

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ISBN 10: 1484270916
ISBN 13:  978-1484270912
Author:  Micheal Lanham

The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.
In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.
By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.

What You Will Learn

• Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)
• Explore variations of GAN
• Understand the basics of other forms of content generation
• Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2

Who This Book Is For
Machine learning developers and AI enthusiasts who want to understand AI content generation techniques

Generating New Reality: From Autoencoders and Adversarial Networks to Deepfakes 1st Table of contents:

Introduction

• Overview of Generative Models in Machine Learning
• Defining the New Reality: Deepfakes and Synthetic Media
• The Evolution of Image and Video Synthesis
• Purpose and Scope of This Book

Chapter 1: Understanding Generative Models

• What Are Generative Models?
• A Historical Perspective on Generative Techniques
• The Importance of Generative Models in AI and Technology
• Key Concepts: Latent Spaces, Sampling, and Data Distribution

Chapter 2: Autoencoders: The Basics of Generating New Data

• Introduction to Autoencoders and Their Components
• How Autoencoders Learn to Encode and Decode Information
• Applications of Autoencoders in Image Generation
• Variational Autoencoders (VAEs): A New Approach to Data Generation

Chapter 3: Generative Adversarial Networks (GANs)

• Introduction to GANs: Two Networks Working Together
• The Role of the Generator and Discriminator
• Training GANs: Challenges and Innovations
• Applications of GANs in Art, Medicine, and Entertainment

Chapter 4: Deepfakes: The Intersection of GANs and Media

• What Are Deepfakes and How Are They Created?
• The Science Behind Deepfake Technology
• The Role of GANs and Autoencoders in Deepfake Generation
• Ethical Issues and the Impact of Deepfakes on Society

Chapter 5: Advanced GAN Architectures

• Deep Convolutional GANs (DCGANs): Revolutionizing Image Generation
• Conditional GANs: Customizing Generated Content
• StyleGAN and Progress in High-Quality Image Synthesis
• Applications of Advanced GANs in Realistic Face Generation and More

Chapter 6: The Role of Deep Learning in Media Generation

• From Static Images to Dynamic Videos: The Evolution of Media Generation
• Using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks for Video Generation
• Cross-Domain Applications of GANs and Autoencoders in Media
• Text-to-Image and Text-to-Video Synthesis: Expanding Creativity

Chapter 7: Deepfake Detection and Mitigation

• Identifying Deepfakes: Techniques and Tools for Detection
• The Arms Race Between Deepfake Creation and Detection
• Ethical Considerations in Using Deepfake Detection Technology
• Legal and Social Implications of Deepfake Technology

Chapter 8: Applications of Generative Models Beyond Deepfakes

• Creative Industries: Art, Music, and Video Production
• AI-Generated Fashion, Architecture, and Design
• Healthcare: Medical Imaging, Drug Discovery, and Personalized Medicine
• Gaming and Virtual Reality: Creating Realistic Environments and Characters

Chapter 9: The Future of Generative Models and Synthetic Media

• Emerging Trends in AI and Media Generation
• The Impact of Generative Models on Media, Entertainment, and Society
• The Role of Ethics and Governance in the Future of AI-Generated Content
• Technological and Regulatory Challenges Ahead

Chapter 10: Building Your Own Generative Models

• Tools and Frameworks for Building Autoencoders and GANs
• Step-by-Step Guide to Creating Your First GAN
• Hands-On Projects: From Training to Application
• Best Practices for Building Ethical and Effective Generative Models

Conclusion

• Reflecting on the Transformative Potential of Generative Models
• Embracing AI’s Role in Shaping New Realities in Media
• The Future of AI and Human Creativity in Content Generation

Appendices

• A. Glossary of Key Terms in AI and Generative Models
• B. Code Examples and Tutorials for Autoencoders and GANs
• C. Resources for Further Reading and Learning
• D. Index

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Micheal Lanham,Generating New,Adversarial Networks