Deep Generative Modeling - MIT 6.S191 Lecture 4

Deep Generative Modeling - MIT 6.S191 Lecture 4

https://www.youtube.com/@AAmini/videos via YouTube Direct link

​ - Introduction

1 of 15

1 of 15

​ - Introduction

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Deep Generative Modeling - MIT 6.S191 Lecture 4

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  1. 1 ​ - Introduction
  2. 2 - Why care about generative models?
  3. 3 ​ - Latent variable models
  4. 4 ​ - Autoencoders
  5. 5 ​ - Variational autoencoders
  6. 6 - Priors on the latent distribution
  7. 7 ​ - Reparameterization trick
  8. 8 ​ - Latent perturbation and disentanglement
  9. 9 - Debiasing with VAEs
  10. 10 ​ - Generative adversarial networks
  11. 11 ​ - Intuitions behind GANs
  12. 12 - Training GANs
  13. 13 - GANs: Recent advances
  14. 14 - CycleGAN of unpaired translation
  15. 15 - Diffusion Model sneak peak

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