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Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
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README.md

Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"

These are notebooks for reproducing our paper "Learning Perceptually-Aligned Representations via Adversarial Robustness" (preprint, blog).

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, Restricted ImageNet
  • 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={ArXiv preprint arXiv:1906.00945},
    year={2019}
}
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