Hands-On Image Generation with TensorFlow
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Hands-On Image Generation with TensorFlow
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Overview of this book
The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles.
Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN.
By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently.
Table of Contents (15 chapters)
Preface
Section 1: Fundamentals of Image Generation with TensorFlow
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Chapter 1: Getting Started with Image Generation Using TensorFlow
Chapter 2: Variational Autoencoder
Chapter 3: Generative Adversarial Network
Section 2: Applications of Deep Generative Models
Chapter 4: Image-to-Image Translation
Chapter 5: Style Transfer
Chapter 6: AI Painter
Section 3: Advanced Deep Generative Techniques
Chapter 7: High Fidelity Face Generation
Chapter 8: Self-Attention for Image Generation
Chapter 9: Video Synthesis
Chapter 10: Road Ahead
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