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a3t's Introduction

A3T

Code for ECML-PKDD 2023 "A3T: Accuracy Aware Adversarial Training"

Usage

CIFAR-10

python train_resnet.py # to test with ResNet18
python train_wideresnet.py # to test with WideResNet

GLUE

python glue.py $dataset $adv_method
  • The dataset parameter can take a value of one of the GLUE datasets.
  • The adv_method parameter must be one of the "clean", "AT", and "A3T" as an adversarial attack method. Each dataset should first run with a 'clean' argument, then one of 'AT' or 'A3T' can be used.

Citing this work

If you use this code in your work, please cite the accompanying paper:

@article{altinisik2022a3t,
  title={A3T: Accuracy Aware Adversarial Training},
  author={Altinisik, Enes and Messaoud, Safa and Sencar, Husrev Taha and Chawla, Sanjay},
  journal={arXiv preprint arXiv:2211.16316},
  year={2022}
}

The vision part of the code is forked from the repository of YisenWang/MART.

a3t's People

Contributors

yisenwang avatar enesaltinisik avatar

Watchers

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