MIT 6.S191: Deep Generative Modeling

By - Alexander Amini
Overview
Practice
Certification
Reminders

Description

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 Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Chapters

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