MIT 6.S191: Deep Generative Modeling
MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
*New 2024 Edition*
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline
0:00 - Introduction
6:10- Why care about generative models?
8:16 - Latent variable models
10:50 - Autoencoders
17:02 - Variational autoencoders
23:25 - Priors on the latent distribution
32:31 - Reparameterization trick
34:36 - Latent perturbation and disentanglement
37:40 - Debiasing with VAEs
39:37 - Generative adversarial networks
42:09 - Intuitions behind GANs
44:57 - Training GANs
48:28 - GANs: Recent advances
50:57 - CycleGAN of unpaired translation
55:03 - Diffusion Model sneak peak
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MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
*New 2024 Edition*
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline
0:00 – Introduction
6:10- Why care about generative models?
8:16 – Latent variable models
10:50 – Autoencoders
17:02 – Variational autoencoders
23:25 – Priors on the latent distribution
32:31 – Reparameterization trick
34:36 – Latent perturbation and disentanglement
37:40 – Debiasing with VAEs
39:37 – Generative adversarial networks
42:09 – Intuitions behind GANs
44:57 – Training GANs
48:28 – GANs: Recent advances
50:57 – CycleGAN of unpaired translation
55:03 – 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!!
