Deep Generative Modeling
Offered By: Alexander Amini via YouTube
Course Description
Overview
Syllabus
Intro
Which face is fake?
Supervised vs unsupervised learning
Why generative models? Outlier detection
Latent variable models
What is a latent variable?
Autoencoders: background
Dimensionality of latent space → reconstruction quality
Autoencoders for representation learning
VAEs: key difference with traditional autoencoder
VAE optimization
Priors on the latent distribution
VAEs computation graph
Reparametrizing the sampling layer
VAEs: Latent perturbation
VAE summary
Generative Adversarial Networks (GANs)
Intuition behind GANS
Progressive growing of GANS (NVIDIA)
Style-based generator: results
Style-based transfer: results
CycleGAN: domain transformation
Deep Generative Modeling Summary
Taught by
https://www.youtube.com/@AAmini/videos
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