
Course 1: Build Basic Generative Adversarial Networks (GANs)
In this course, you will: Learn about GANs and their applications; Understand the intuition behind the fundamental components of GANs; Explore and implement multiple GAN architectures; Build conditional GANs capable of generating examples from determined categories.
Week 1: Intro to GANs
- Learn about GANs and their applications, understand the intuition behind the basic components of GANs, and build your very own GAN using PyTorch.
Week 2: Deep Convolutional GAN
- Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images.
Week 3: Wasserstein GANs with Normalization
- Reduce instances of GANs failure due to imbalances between the generator and discriminator by learning advanced techniques such as WGANs to mitigate unstable training and mode collapse with a W-Loss and an understanding of Lipschitz Continuity.
Week 4: Conditional and Controllable GANs
- Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories.