Welcome to the Introduction to Deep Generative Models
In less than a decade, generative models have produced astonishing results in a wide area of applications including image (conditional) generation, text & speech synthesis to name a few. In this course, we will cover six main families of generative models:
- Deep Autoregressive Models
- Normalizing Flows
- Variational Autoencoders
- Energy-based Models
- Probabilistic Diffusion Models
- Generative Adversarial Networks
You will learn about:
- The mathematical foundations for each family
- Their properties, their pros & cons
- Use-cases & areas of application
- How to train them via PyTorch implementations