Training GANs for Image Inpainting with PyTorch

Image restoration and editing have been revolutionized by deep learning, specifically Generative Adversarial Networks (GANs). This written course guides you through the core concepts of image inpainting, showing you how to fill in missing or corrupted parts of an image realistically. You will transition from understanding basic generative models to writing complete training pipelines in PyTorch. By studying structured explanations and clear code snippets, you will learn how generators and discriminators work together to rebuild visual data. What you'll learn: Understand the foundational architecture of Generative Adversarial Networks (GANs) and how they apply to computer vision; Configure coarse-to-fine generators to handle both global structure and local textures; Apply advanced loss functions, including Wasserstein loss with gradient penalty, for stable model training; Write clean, modular training loops using modern PyTorch conventions and type hinting; Evaluate inpainting quality using standard image reconstruction metrics. The course begins with essential terminology and the mathematical foundations of generative modeling before moving step-by-step through network design, loss formulation, and practical training strategies. You will read through detailed code implementations and learn how to troubleshoot common training challenges like mode collapse. Designed for developers, data science enthusiasts, and students who have a basic understanding of Python and neural networks, but are new to generative deep learning. No advanced mathematics or prior GAN experience is required. Start reading today to build your own image restoration models from scratch.