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Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"
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sample_inputs
README.md
generation.ipynb
headline.jpg
image_to_image_translation.ipynb
inpainting.ipynb
paint_with_features.ipynb
requirements.txt
sketch_to_image.ipynb
superresolution.ipynb
user_constants.py

README.md

Code for "Computer Vision with a Single (Robust) Classifier"

These are notebooks for reproducing our paper "Computer Vision with a Single (Robust) Classifier" (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, ImageNet, Horse-to-Zebra, Summer-to-Winter, Apple-to-Orange
  • 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 paths in user_constants.py to point to PyTorch-formatted versions of the CIFAR and ImageNet datasets
  • Start a jupyter notebook server: jupyter notebook . --ip 0.0.0.0

Citation

@inproceedings{santurkar2019computer,
    title={Computer Vision with a Single (Robust) Classifier},
    author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
    booktitle={ArXiv preprint arXiv:1906.09453},
    year={2019}
}
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