9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)
Channel: MLVU
Date: 2019-03-05

slides: https://mlvu.github.io/lectures/51.Deep%20Learning2.annotated.pdf
course materials: https://mlvu.github.io

Today we discuss neural networks that can generate things that are similar to the data they were fed: generative models. We start out with a discussion of how to define a generator neural network, and why we can't train them in a straightforward way. Then we discuss two training approaches: generative adversarial networks and (variational) autoencoders.

For the variational autoencoder, we derive the whole loss function from first principles, showing how it approximates a maximum likelihood fit on the data.