PyTorch Code used in the paper Introduction to Deep Generative Modeling:

@article{RuthottoHaber2021,
  title = {An Introduction to Deep Generative Modeling},
  year = {2021},
  journal = {arXiv preprint arXiv:tbd},
  author = {L. Ruthotto and E. Haber},
  pages = {25 pages},
  url={https://arxiv.org/abs/2103.05180}
}

To reproduce the examples from the paper (up to randomization), we provide a shell script runAll.sh.

The examples directory contains interactive version of the examples from the paper. Those can be run locally or using Google Colab. For the latter option, you may click the badges below:

1. Two-Dimensional Normalizing Flow Examples with Real NVP
2. Two-Dimensional Continuous Normalizing Flow Example with OT-Flow
3. Variational Autoencoder for MNIST Image Generation
4. DCGAN for MNIST Image Generation
5. WGAN for MNIST Image Generation

The code is based on pytorch and some other standard machine learning packages. In addition, training the continuous normalizing flow example requires OT-Flow.

This material is in part based upon work supported by the National Science Foundation under Grant Number 1751636, the Air Force Office of Scientific Research under Grant Number 20RT0237, and the US DOE's Office of Advanced Scientific Computing Research Field Work Proposal 20-023231. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.