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Generalist: Decoupling Natural and Robust Generalization

Official implementation for CVPR'23 paper "Generalist: Decoupling Natural and Robust Generalization"

Prerequisites

Training and Testing

  $ CUDA_VISIBLE_DEVICES={your GPU number} python3 main.py 
  $ CUDA_VISIBLE_DEVICES={your GPU number} python3 main.py --arch 'WRN32'

Then, it will automatically run all the robustness evaluation in our paper, including NAT, PGD20/100, MIM, CW, APGD<sub>ce</sub>, APGD<sub>dlr</sub>, APGD<sub>t</sub>, FAB<sub>t</sub>, Square and AutoAttack.

Pretrained model

Citation

If you are interested in our work, please consider citing the related paper:

@inproceedings{wang2023simple,
  title={Generalist: Decoupling Natural and Robust Generalization},
  author={Hongjun Wang and Yisen Wang},
  booktitle={CVPR},
  year={2023}
}

@inproceedings{wang2022selfensemble,
  title={Self-ensemble Adversarial Training for Improved Robustness},
  author={Hongjun Wang and Yisen Wang},
  booktitle={ICLR},
  year={2022}
}