MIT 6.S191 (2024): Deep Generative Modeling @AAmini
MIT 6.S191 (2024): Deep Generative Modeling @AAmini
Uploaded May 2024 | Updated September 2026, 2 weeks ago
MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
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!!
MIT 6.S191 (2024): Deep Generative ModelingMIT Introduction to Deep Learning (2024) | 6.S191MIT 6.S191 (2023): Deep Learning New FrontiersMIT 6.S191 (2018): Faster ML Development with TensorFlowMIT 6.S191 (2025): Recurrent Neural Networks, Transformers, and AttentionMIT 6.S191 (2019): Convolutional Neural NetworksMIT 6.S191 (2025): Language Models and New FrontiersMIT Introduction to Deep Learning | 6.S191MIT 6.S191 (2019): Deep Learning Limitations and New FrontiersMIT 6.S191 (2018): Introduction to Deep LearningMIT 6.S191 (2018): Deep Generative ModelingCo-Learning of Task and Sensor Placement for Soft Robotics (Teaser)
Alexander Amini |

MIT 6.S191 (2024): Deep Generative Modeling

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER