In this video, you'll see a different underlying cost function called Earth mover's distance, that measures the distance between two distributions and generally outperforms the one associated with BCE loss for training GANs. Even though there is an infinite number of decimal values between zero and one, the discriminator, as it improves, will be pushing towards those ends.

When using BCE loss to train a GAN, you often encounter mode collapse, and vanishing gradient problems due to the underlying cost function of the whole architecture. 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.

Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.īuild a comprehensive knowledge base and gain hands-on experience in GANs. 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. Build conditional GANs capable of generating examples from determined categories Explore and implement multiple GAN architectures Understand the intuition behind the fundamental components of GANs Learn about GANs and their applications

Earth mover distance