This lecture introduces deep generative modeling through latent variable models, autoencoders, variational autoencoders, and generative adversarial networks. It also briefly previews diffusion models and discusses disentanglement, debiasing, conditioning, and unpaired translation.

Syllabus

- Introduction
- Why care about generative models?
- Latent variable models
- Autoencoders
- Variational autoencoders
- Priors on the latent distribution
- Reparameterization trick
- Latent perturbation and disentanglement
- Debiasing with VAEs
- Generative adversarial networks
- Intuitions behind GANs
- Training GANs
- GANs: Recent advances
- Conditioning GANs on a specific label
- CycleGAN of unpaired translation
- Summary of VAEs and GANs
- Diffusion Model sneak peak