On Memorization in Probabilistic Deep Generative Models

This repository contains the code necessary to reproduce the experiments in On Memorization in Probabilistic Deep Generative Models. You can also use this code to measure memorization in other types of probabilistic deep generative models. If you use our code in your own work please cite the paper using, for instance, the following BibTeX entry:

@article{van2021memorization,
  title={On Memorization in Probabilistic Deep Generative Models},
  author={{Van den Burg}, G. J. J. and Williams, C. K. I.},
  journal={arXiv preprint arXiv:2106.03216},
  year={2021}
}

If you have any questions or encounter an issue when using this code, please send an email to gertjanvandenburg at gmail dot com.

Introduction

The files in the scripts directory are needed to reproduce the experiments and generate the figures in the paper. The experiments are organized using the Makefile provided. To reproduce the experiments or recreate the figures from the analysis, you'll have to install a number of dependencies. We use PyTorch to implement the deep learning algorithms. If you don't wish to re-run all the models, you can download the result files used in the paper (see below).

The scripts are all written in Python, and the necessary external dependencies can be found in the requirements.txt file. These can be installed using:

$ pip install -r requirements.txt

To recreate the figures the following system dependencies are also needed: pdflatex, latexmk, lualatex, and make. These programs are available for all major platforms.

Reproducing the results

To train the models on the different data sets, you can run:

$ make memorization

Note that depending on your machine this may take some time, so it might be easier to simply download the result files instead. It is also worth mentioning that while we have made an effort to ensure reproducibility by setting the random seed in PyTorch, platform or package version differences may result in slightly different output files (see also PyTorch Reproducibility).

All figures in the paper are generated from the raw result files using Python scripts. First, the summarize.py script takes the raw result files and creates summary files for each data set. Next, the analysis scripts are used to generate the figures, most of which are LaTeX files that require compilation using PDFLaTeX or LuaLaTeX. Simply run:

$ make analysis

to create the summaries and the output files. When using the result files linked below this will give the exact same figures as shown in the paper.

Result files

Due to their size, the raw result files are not contained in this repository, but can be downloaded separately from this link (about 2.6GB). After downloading the results.zip file, unpack it and move the results directory to where you've cloned this repository (so adjacent to the scripts directory). Below is a concise overview of the necessary commands:

$ git clone https://github.com/alan-turing-institute/memorization
$ cd memorization
$ wget https://gertjanvandenburg.com/projects/memorization/results.zip # or download the file in some other way
$ unzip results.zip
$ touch results/*/*/*          # update modification time of the result files
$ make analysis                # optionally, run ``make -n analysis`` first to see what will happen

After unpacking the zip file, you can optionally verify the integrity of the results using the SHA-256 checksums provided:

$ sha256sum --check results.sha256

License

The code in this repository is licensed under the MIT license. See the LICENSE file for further details. Reuse of the code in this repository is allowed, but should cite our paper.

Notes

If you find any problems or have a suggestion for improvement of this repository, please let me know as it will help make this resource better for everyone.