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Deepmind Team
72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
# Unsupervised Adversarial Training (UAT)
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This repository contains the trained model and dataset used for Unsupervised
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Adversarial Training (UAT) from the paper
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[Are Labels Required for Improving Adversarial Robustness?](https://arxiv.org/abs/1905.13725)
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## Contents
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This repo serves two primary functions:
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* Data release: We share indices for the 80 Million Tiny Images Dataset subset
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used in our experiments, and a utility for loading the data.
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* Model release: We have released our top-performing model on TF-Hub, and
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include an example demonstrating how to use it.
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## Running the code
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### Using the model
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Our model is available via
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[TF-Hub](https://tfhub.dev/deepmind/unsupervised-adversarial-training/cifar10/wrn_106/1).
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For example usage, refer to `quick_eval_cifar.py`. The preferred method of
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running this script is through `run.sh`, which will set up a virtual
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environment, install the dependendencies, and run the evaluation script, which
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will print the adversarial accuracy of the model.
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```bash
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cd /path/to/deepmind_research
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unsupervised_adversarial_training/run.sh
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```
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### Viewing the dataset
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First, download the 80 Million Tiny Images Dataset image binary from the
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official web page: http://horatio.cs.nyu.edu/mit/tiny/data/index.html
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Note this file is very large, and requires 227 GB of disc space.
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The file `tiny_200K_idxs.txt` indicates which images from the dataset form the
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80M@200K training set used in the paper. For example usage, refer to
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`save_example_images.py`.
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To view example images from this dataset, use the command:
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```bash
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cd /path/to/deepmind_research
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python -m unsupervised_adversarial_training.save_example_images \
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--data_bin_path=/path/to/tiny_images.bin
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```
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This will save the first 100 images to the directory
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`unsupervised_adversarial_training/images`.
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## Citing this work
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If you use this code in your work, please cite the accompanying paper:
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```
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@inproceedings{uat2019,
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title={Are Labels Required for Improving Adversarial Robustness?},
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author={Jonathan Uesato and Jean-Baptiste Alayrac and Po-Sen Huang and
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Robert Stanforth and Alhussein Fawzi and Pushmeet Kohli},
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booktitle={Advances in Neural Information Processing Systems},
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year={2019}
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}
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```
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## Disclaimer
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This is not an official Google product.
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