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

Alexander Amini

MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
2023 Edition

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00​ - Introduction
5:48 - Why care about generative models? 
7:33​ - Latent variable models
9:30​ - Autoencoders
15:03​ - Variational autoencoders
21:45 - Priors on the latent distribution
28:16​ - Reparameterization trick
31:05​ - Latent perturbation and disentanglement 
36:37 - Debiasing with VAEs
38:55​ - Generative adversarial networks
41:25​ - Intuitions behind GANs
44:25 - Training GANs
50:07 - GANs: Recent advances
50:55 - Conditioning GANs on a specific label
53:02 - CycleGAN of unpaired translation
56:39​ - Summary of VAEs and GANs
57:17 - Diffusion Model sneak peak

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MIT 6.S191: Deep Generative Modeling

MIT 6.S191: Deep Generative Modeling

MIT 6.S191: Deep Generative Modeling

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