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Shibani Santurkar authored and andrewilyas committed May 29, 2019
0 parents commit be86231c0fcb5e233a596426bd437b15efd817db
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[submodule "robustness_lib"]
path = robustness_lib
url = git@github.com:MadryLab/robustness_lib.git
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# Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"

These are notebooks for reproducing our paper "Learning
Perceptually-Aligned Representations via Adversarial Robustness"
([preprint](http://gradsci.org/paper.pdf),
[blog](http://gradsci.org/robust_reps)).

![](headline.jpg)

## Running the notebooks

Steps to run the notebooks (for now, requires CUDA):
- Clone this repository with `--recurse-submodules` to include submodules (`--recursive` pre-Git 2.13)
- Download our models from S3: [CIFAR-10](http://andrewilyas.com/CIFAR.pt), [Restricted ImageNet](http://andrewilyas.com/RestrictedImageNet.pt)
- Make a `models` folder in the main repository folder, and save the
checkpoints there
- Install all the required packages with `pip install -r requirements.txt`
- Edit `user_constants.py` to point to PyTorch-formatted versions of the `CIFAR` and `ImageNet` datasets
- Start a jupyter notebook server: `PYTHONPATH=robustness_lib/ jupyter notebook . --ip 0.0.0.0`

## Citation

```
@inproceedings{engstrom2019learning,
title={Learning Perceptually-Aligned Representations via Adversarial Robustness},
author={Logan Engstrom and Andrew Ilyas and Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Aleksander Madry},
booktitle={Preprint},
year={2019}
}
```

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torch
torchvision
pandas
numpy
scipy
GPUtil
dill
tensorboardX
tables
tqdm
Submodule robustness_lib added at 48e8fd
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# Path to dataset (Insert appropriate paths here)
DATA_PATH_DICT = {
'CIFAR': '/path/to/cifar',
'RestrictedImageNet': '/path/to/imagenet'
}

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