Image-to-Image Translation with Pix2pix and CycleGAN

Transforming summer scenes into winter landscapes or sketches into realistic photos is no longer magic—it is the power of image-to-image translation. Understanding how these generative frameworks operate is essential for anyone entering the field of modern computer vision. In this text-only course, you will explore the core concepts of Generative Adversarial Networks (GANs) specialized in image synthesis. You will learn how both paired datasets and unpaired collections are processed, trained, and evaluated. What you will learn: Understand the foundational architecture of Generative Adversarial Networks (GANs); Compare paired training in Pix2pix with unpaired training in CycleGAN; Learn the mechanics of cycle consistency loss and generator-discriminator dynamics; Explore high-resolution image synthesis concepts using Pix2pixHD; Evaluate model performance using modern metrics like Frechet Inception Distance (FID); Address common training challenges such as mode collapse and instability. You will start with key terminology and foundational concepts before moving into the architectural details and loss functions. Through clear written explanations and conceptual code snippets, you will grasp how to design and evaluate these powerful generative pipelines. This course is designed for beginners in machine learning and deep learning, with no prior experience in generative models required. Start learning the foundations of generative image synthesis today.