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Generative Adversarial Networks (GANs)
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Classroom Contents
Deep Generative Modeling
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- 1 Intro
- 2 Supervised vs unsupervised learning Supervised Learning Unsupervised Learning
- 3 Generative modeling Goal Take as input training samples from some distribution and learn a model that represents that distribution
- 4 Why generative models? Debiasing
- 5 Why generative models? Outlier detection
- 6 What is a latent variable?
- 7 Autoencoders: background
- 8 Dimensionality of latent space reconstruction quality
- 9 Autoencoders for representation learning
- 10 Traditional autoencoders
- 11 VAEs: key difference with traditional autoencoder
- 12 VAE optimization
- 13 Intuition on regularization and the Normal prior
- 14 Reparametrizing the sampling layer
- 15 Why latent variable models? Debiasing
- 16 Generative Adversarial Networks (GANs)
- 17 Intuition behind GANS
- 18 Training GANs: loss function
- 19 GANs for image synthesis: latest results
- 20 Applications of paired translation
- 21 Paired translation: coloring from edges
- 22 Distribution transformations GANG
