Generative 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.

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Spis treści

Hands-On Generative Adversarial Networks with PyTorch 2.x. Gain hands-on expertise in utilizing robust Generative AI models to tackle a wide array of challenges - Second Edition eBook -- spis treści

• 1. Basics of Generative Models
• 2. Getting Started with PyTorch 2.0
• 3. Useful tricks for model designing
• 4. Building your first GAN with PyTorch
• 5. Interactively generating images via Conditional GAN
• 6. Producing Photorealistic images with StyleGAN2/3
• 7. Image-to-image translation and its applications
• 8. Image restoration with GANs
• 9. Training your GANs to break other people’s models
• 10. Image generation from description text
• 11. Sequence synthesis with GANs
• 12. Video Generation with GANs
• 13. Reconstructing 3D models with GANs
• 14. GANs for Imitation Learning and Other Automation Tasks
• 15. Hybrid models: Using GANs in combination with Transformers or Diffusion Models
• 16. Where generative models are heading?
• 17. Commercial Use and Ethical Considerations when deploying generative models