Generating a new reality : from autoencoders and adversarial networks to deepfakes
- Micheal Lanham.
- text file
- New York : Apress, 
- Copyright notice
- Physical description
- 1 online resource (xvii, 321 pages) : illustrations
- Lanham, Micheal, author.
- Chapter 1: Deep Learning PerceptronChapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.No of pages: 30Sub -Topics1. Understanding deep learning and supervised learning.1. Using the perceptron for supervised learning.2. Constructing a multilayer perceptron.3. Discover the basics of activation, loss, optimization and back propagation for problems of regression and classification. Chapter 2: Unleashing Autoencoders and Generative Adversarial NetworksChapter Goal: This chapter introduces the autoencoder and GAN for simple content generation. Along the way we also learn about using convolutional network layers for better feature extraction.No of pages: 30Sub - Topics 1. Why we need autoencoders and how they function.2. Improving on the autoencoder with convolutional network layers.3. Generating content with the GAN.4. Explore methods for improving on the vanilla GAN. Chapter 3: Exploring the Latent SpaceChapter Goal: In this chapter we discover the latent space in AI. What it means to move through the AI latent space using variational autoencoders and conditional GANs.No of pages : 30 Sub - Topics: 1. Understanding variation and the variational autoencoder.2. Exploring the latent space with a VAE.3. Extending a GAN to be conditional.4. Generate interesting foods using a conditional GAN. Chapter 4: GANs, GANs and More GANsChapter Goal: In this chapter we begin uncovering the vast variations in GANs and their applications. We start with basics like the double convolution GAN and work up to the Stack and Progressive GANs.No of pages: 30Sub - Topics: 1. Look at samples from the many variations of GANs.2. Setup and use a DCGAN.3. Understand how a StackGAN works.4. Work with and use a ProGAN. Chapter 5: Image to Image Translation with GANs
- Covers: Pix2Pix and DualGAN, side projects for understanding with ResNET and UNET, advanced network architectures for image classification/generation
- Chapter 6: Translating Images with Cycle Consistency
- Covers: Cycle consistency loss and the CycleGAN, BiCycleGAN and StarGAN
- Chapter 7: Styling with GANs
- Covers: StyleGAN, Attention and the Self-attention GAN with a look at DeOldify
- Chapter 8: Developing DeepFakesChapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project. No of pages: 301. Learn how to isolate faces or other points of interest in images or video.2. Extract and replace faces from images or video.3. Use DeepFakes GAN to generate facial images based on input image.4. Put it all together and allow the user to generate their own DeepFake video. Chapter 9: Uncovering Adversarial Latent AutoencodersChapter Goal: GANs are not the only technique that allows for content manipulation and generations. In this chapter we look at the ALAE method for generating content.No of pages: 1. Look at how to extend autoencoders for adversarial learning.2. Understanding how AE can be used to explore the latent space in data.3. Use ALAE to generate conditional content.4. Revisit our previous foods example and see what new foods we can generate. Chapter 10: Video Content with First Order Model MotionChapter Goal: In this chapter we explore a new technique for animating static images called First Order Model Motion. At the end of this chapter we will use this technique to create avatars for Skype or Zoom.No of pages: 30 1. Discover the basic of First Order Model Motion, what it is and how it works.2. Be able to apply FOMM to a number of static image datasets for various applications.3. Use the project Avatarify for generating real-time avatars from static avatars.4. Use Avatarify real-time in applications like Zoom or Skype.
- (source: Nielsen Book Data)
- Publisher's summary
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.
(source: Nielsen Book Data)
- Publication date
- Includes index.
- 9781484270929 (electronic bk.)
- 1484270924 (electronic bk.)
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