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VAE summary
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Classroom Contents
Deep Generative Modeling
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- 1 Intro
- 2 Which face is fake?
- 3 Supervised vs unsupervised learning
- 4 Why generative models? Outlier detection
- 5 Latent variable models
- 6 What is a latent variable?
- 7 Autoencoders: background
- 8 Dimensionality of latent space → reconstruction quality
- 9 Autoencoders for representation learning
- 10 VAEs: key difference with traditional autoencoder
- 11 VAE optimization
- 12 Priors on the latent distribution
- 13 VAEs computation graph
- 14 Reparametrizing the sampling layer
- 15 VAEs: Latent perturbation
- 16 VAE summary
- 17 Generative Adversarial Networks (GANs)
- 18 Intuition behind GANS
- 19 Progressive growing of GANS (NVIDIA)
- 20 Style-based generator: results
- 21 Style-based transfer: results
- 22 CycleGAN: domain transformation
- 23 Deep Generative Modeling Summary
