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

For all lectures, slides, and lab materials: [ Ссылка ]

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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