Image-to-Image Translation with Pix2Pix: Train and Evaluate GANs

Translating sketches to realistic photos or transforming day scenes to night requires powerful generative models. Pix2Pix GANs offer a robust framework to map input images to target outputs using paired datasets. This text-based course guides you through the core concepts of Generative Adversarial Networks, specifically focusing on the Pix2Pix architecture, so you can confidently train, optimize, and evaluate your own translation models. What you'll learn: - Understand the foundational architecture of Pix2Pix, including U-Net generators and PatchGAN discriminators - Prepare and preprocess paired image datasets for conditional GAN training - Implement the Pix2Pix training loop using modern deep learning best practices - Evaluate generator performance using quantitative metrics and qualitative assessment - Apply optimization techniques such as mixed-precision training to improve efficiency - Identify and resolve common GAN training issues like mode collapse and instability The course begins with key terminology and foundational generative concepts before guiding you through written explanations of the training loop, loss functions, and evaluation metrics. It is designed for beginners in computer vision and deep learning who want to understand generative models, with no prior GAN experience required. Start reading to explore the world of image-to-image translation today.