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