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MIT 6.S191 (2023): Deep Generative Modeling

Alexander Amini
318K subscribers
318K views 2 years ago
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!! ...more
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Introduction

0:00

Why care about generative models?

5:48

Latent variable models

7:33

Autoencoders

9:30

Variational autoencoders

15:03

Priors on the latent distribution

21:45

Reparameterization trick

28:16

Latent perturbation and disentanglement

31:05

Debiasing with VAEs

36:37

Generative adversarial networks

38:55

Intuitions behind GANs

41:25

Training GANs

44:25

GANs: Recent advances

50:07

Conditioning GANs on a specific label

50:55

CycleGAN of unpaired translation

53:02

Summary of VAEs and GANs

56:39

Diffusion Model sneak peak

57:17
MIT 6.S191 (2023): Deep Generative Modeling
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!!

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Alexander Amini

318K subscribers