29 March - 23 April 2019
This talk will be a hands-on live coding tutorial. We will implement a Generative Adversarial Network (GAN) to learn to generate small images.
GANs are a relatively recent development in unsupervised learning and generative modeling, where we want to learn the distribution of our data. Instead of fitting an explicit density model (with strong assumptions on the data distribution), GANs generate samples, defining an implicit density model. They are able to generate sharp samples from a (meaningful?) continuous latent space.
We will assume only a superficial familiarity with deep learning and a notion of PyTorch. We will make this tutorial as self-contained as possible. The goal is that this talk/tutorial can serve as an introduction to PyTorch at the same time as being an introduction to GANs.