Reading Assignment 6: Advanced VAEs and GANs

This reading is intended to cover advanced architectures that we did not have time for in the respective sessions. You are not expected to read all these papers in detail, but try to read some introductions, conclusions, and figures.

VAEs

• Neural Discrete Representation Learning aka VQ-VAE
• VQ-VAE 2 for very impressive autoencoder-based results
• NVAE: A Deep Hierarchical Variational Autoencoder

GANs

• Progressive Growing
• StyleGAN
• StyleGAN 2
• StyleGAN 3

This is an excellent series of papers, doing a great job at explaining their choices. They explain architectures as well as all the “little tricks” that really make it work.

Optional Reading

This is a series on different GAN losses and the gap between theory and practice:

• Wasserstein GAN
• Improved Wasserstein GAN
• Wasserstein GANs Work Because They Fail