Preface
This phrase best describes image generation using artificial intelligence (AI). The field of deep learning—a subset of artificial intelligence—has been developing rapidly in the last decade. Now we can generate artificial but faces that are indistinguishable from real people's faces, and to generate realistic paintings from simple brush strokes. Most of these abilities are owed to a type of deep neural network known as a generative adversarial network (GAN). With this hands-on book, you'll not only develop image generation skills but also gain a solid understanding of the underlying principles.
The book starts with an introduction to the fundamentals of image generation using TensorFlow covering variational autoencoders and GANs. As you progress through the chapters, you'll learn to build models for different applications for performing face swaps using deep fakes, 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 image generative technologies.