This repository contains the trained model and dataset used for Unsupervised Adversarial Training (UAT) from the paper Are Labels Required for Improving Adversarial Robustness?

This repo serves two primary functions:

• Data release: We share indices for the 80 Million Tiny Images Dataset subset used in our experiments, and a utility for loading the data.
• Model release: We have released our top-performing model on TF-Hub, and include an example demonstrating how to use it.

Our model is available via TF-Hub. For example usage, refer to quick_eval_cifar.py. The preferred method of running this script is through run.sh, which will set up a virtual environment, install the dependendencies, and run the evaluation script, which will print the adversarial accuracy of the model.

cd /path/to/deepmind_research
unsupervised_adversarial_training/run.sh

First, download the 80 Million Tiny Images Dataset image binary from the official web page: http://horatio.cs.nyu.edu/mit/tiny/data/index.html

Note this file is very large, and requires 227 GB of disc space.

The file tiny_200K_idxs.txt indicates which images from the dataset form the 80M@200K training set used in the paper. For example usage, refer to save_example_images.py.

To view example images from this dataset, use the command:

cd /path/to/deepmind_research
python -m unsupervised_adversarial_training.save_example_images \
  --data_bin_path=/path/to/tiny_images.bin

This will save the first 100 images to the directory unsupervised_adversarial_training/images.

If you use this code in your work, please cite the accompanying paper:

@inproceedings{uat2019,
  title={Are Labels Required for Improving Adversarial Robustness?},
  author={Jonathan Uesato and Jean-Baptiste Alayrac and Po-Sen Huang and
  Robert Stanforth and Alhussein Fawzi and Pushmeet Kohli},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

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