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Decoupled Kullback-Leibler (DKL) Divergence Loss

This repository contains the implementation code for our NeurIPS 2024 paper Decoupled Kullback-Leibler (DKL) Divergence Loss, arXiv.

Results and Pretrained Models for Adversarial Robustness

By 2023/05/20, with IKL loss, we achieve new state-of-the-art adversarial robustness under settings that with/without augmentation strategies on auto-attack.

CIFAR-100 with autoaug

# Method Model Natural Acc Robust Acc (AutoAttack) link log
1 DAJAT WRN-34-10 68.74 31.30 - -
2 IKL-AT WRN-34-10 65.93 32.52 model log

CIFAR-100 with basic data preprocessing (random crop and random horizontal flip)

# Method Model Natural Acc Robust Acc (AutoAttack) link log
1 AWP WRN-34-10 60.38 28.86 - -
2 LBGAT WRN-34-10 62.31 29.33 - -
3 LAS-AT WRN-34-10 62.99 30.77 - -
4 ACAT WRN-34-10 65.75 30.23 - -
5 IKL-AT WRN-34-10 66.51 31.43 model log
6 IKL-AT WRN-34-10 65.76 31.91 model log

CIFAR-100 with synthesized data

# Method Model Natural Acc Robust Acc (AutoAttack) link log
1 Wang et al. (better diffusion models) 1M WRN-28-10 68.06 35.65 - -
2 Wang et al. (better diffusion models) 50M WRN-28-10 72.58 38.83 - -
3 IKL-AT 1M WRN-28-10 68.99 35.89 - -
4 IKL-AT 50M WRN-28-10 73.85 39.18 model log

CIFAR-10 with basic data preprocessing (random crop and random horizontal flip)

# Method Model Natural Acc Robust Acc (AutoAttack) link log
1 AWP WRN-34-10 85.36 56.17 - -
2 LBGAT WRN-34-20 88.70 53.57 - -
3 LAS-AT WRN-34-10 87.74 55.52 - -
4 ACAT WRN-34-10 82.41 55.36 - -
5 IKL-AT WRN-34-10 85.31 57.13 model log

CIFAR-10 with synthesized data

# Method Model Natural Acc Robust Acc (AutoAttack) link log
1 Wang et al. (better diffusion models) 1M WRN-28-10 91.12 63.35 - -
2 Wang et al. (better diffusion models) 20M WRN-28-10 92.44 67.31 - -
3 IKL-AT 1M WRN-28-10 90.75 63.54 - -
4 IKL-AT 20M WRN-28-10 92.16 67.75 model log

Training

More training scripts will be provided soon to reproduce our results on knowledge distillation and adversarial training tasks.

For the adversarial training task:
cd Adv-training-dkl 
bash sh/train_dkl_cifar100.sh
bash sh/train_dkl_cifar100_autoaug.sh
bash sh/train_dkl_cifar10.sh

Evaluation

before running the evaluation with auto-attack, please download the pre-trained models.

cd Adv-training-dkl/auto_attacks
bash sh/eval.sh

Contact

If you have any questions, feel free to contact us through email ([email protected]) or Github issues. Enjoy!

BibTex

If you find this code or idea useful, please consider citing our related work:

@article{cui2023decoupled,
  title={Decoupled Kullback-Leibler Divergence Loss},
  author={Cui, Jiequan and Tian, Zhuotao and Zhong, Zhisheng and Qi, Xiaojuan and Yu, Bei and Zhang, Hanwang},
  journal={arXiv preprint arXiv:2305.13948},
  year={2023}
}

@inproceedings{cui2021learnable,
  title={Learnable boundary guided adversarial training},
  author={Cui, Jiequan and Liu, Shu and Wang, Liwei and Jia, Jiaya},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={15721--15730},
  year={2021}
}