MadryLab / Robust_representations
Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
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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, blog). Based on the robustness python library.
Running the notebooks
Steps to run the notebooks (for now, requires CUDA):
- Clone this repository
- Download our models from S3: CIFAR-10, Restricted ImageNet (standard training for comparison)
- 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 theCIFAR
andImageNet
datasets - Start a jupyter notebook server:
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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