Hands-On Generative Adversarial Networks with PyTorch 2.x

Explore the creation of AI-generated images, videos, and audio, and gain proficiency in designing, training, and optimizing various types of generative networks for diverse applications using PyTorch

Key Features

Implement GAN architectures to generate images, text, audio, 3D models, and more
Understand how GANs work and become an active contributor in the open-source community
Learn about using GANs in combination with other generative models, such as Transformers and Diffusion Models

Book DescriptionGenerative AI is the most spoken of AI direction in media nowadays, and this book is aimed at assisting you in becoming an expert in its most well-established class of models - Generative Adversarial Nets.

With the help of this book, you will work your way up from understanding the basic components and architecture of GANs, building your first model from scratch to designing, building, training and optimizing a wide variety of these powerful models. You will go way beyond theoretical knowledge and gain hands-on experience in finding the right type of GAN for each specific problem using PyTorch examples provided in every chapter.

You will cover important image-generation and translation architectures such as classic and conditional GANs, DCGANs, StyleGANs, CycleGANs, and pix2pix. Learn to synthesize sequences, text and audio, and generate videos. Finally, we will dive into the state-of-the-art hybrid models of GANs with other generative models.

By the end of this book, you will be an expert in practical applications of GANs to real-world problems.What you will learn

Use PyTorch's latest features to ensure efficient model design
Get to grips with the working mechanisms of GAN models
Build and train a range of GANs to perform a variety of image synthesis and editing operations
Perform style transfer between unpaired and unpaired image collections with CycleGAN and pix2pix
Train GANs for video generation and video-to-video translation
Acquire the skills to use GANs for imitation learning and other automation tasks
Understand how to use elements of GANs in combination with other generative models
Attain a comprehensive understanding of the privacy and ethical considerations

Who this book is forThis GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You'll become familiar with state-of-the-art GAN architecture with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.