Apply Generative Adversarial Networks (GANs)

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

• Week 1: GANs for Data Augmentation and Privacy
• Learn different applications of GANs, understand the pros/cons of using them for data augmentation, and see how they can improve downstream AI models!
• Week 2: Image-to-Image Translation with Pix2Pix
• Understand image-to-image translation, learn about different applications of this framework, and implement a U-Net generator and Pix2Pix, a paired image-to-image translation GAN!
• Week 3: Unpaired Translation with CycleGAN
• Understand how unpaired image-to-image translation differs from paired translation, learn how CycleGAN implements this model using two GANs, and implement a CycleGAN to transform between horses and zebras!