DS-Net

Code accompanying the paper
Learning Diverse-Structured Networks for Adversarial Robustness paper

Abstract

In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST). Classic network architectures (NAs) are generally worse than searched NAs in ST, which should be the same in AT. In this paper, we argue that NA and AT cannot be handled independently, since given a dataset, the optimal NA in ST would be no longer optimal in AT. That being said, AT is time-consuming itself; if we directly search NAs in AT over large search spaces, the computation will be practically infeasible. Thus, we propose a diverse-structured network (DS-Net), to significantly reduce the size of the search space: instead of low-level operations, we only consider predefined atomic blocks, where an atomic block is a time-tested building block like the residual block. There are only a few atomic blocks and thus we can weight all atomic blocks rather than find the best one in a searched block of DS-Net, which is an essential trade-off between exploring diverse structures and exploiting the best structures. Empirical results demonstrate the advantages of DS-Net, i.e., weighting the atomic blocks.

Requirements

• Pytorch==1.7.0, torchvision==0.8.1
• Python==3.8.5
• AutoAttack

Training and Evaluation

On CIFAR-10 and SVHN:

python train_ds_net.py --gpu x --start_evaluation 0 --note xxx --factor 4 --init_channel 20 --trades_beta 6 --is_trades 0 --is_mart 0 --is_at 0 --epsilon 0.031 --is_softmax 1 --is_normal 1 --use_amp 1 --epochs 120 --step_size 0.007 --seed xx

• Use "is_trades", "is_at" and "is_mart" to switch among three adversarial training styles.

• Use "start_evaluation" to specify which epoch you would like to start evaluation.

• Use "is_softmax" to determine whether the attention weights should be learned.

• Use "is_normal" and so on to select the initialization for the attention weights.

• Use "use_amp" to determine whether you would like to use mixed precision training. To use mixed-precision training, follow the apex installation instructions here

• Adversarial training detailes can be selected by changing "trades_beta", "epsilon", "epochs", "step_size", etc.

• DS-Net detailes can be selected by changing "factor" and "init_channel".

Citation

If you use any part of this code in your research, please cite our paper:

@article{du2021dsnet,
  title={Learning Diverse-Structured Networks for Adversarial Robustness},
  author={Du, Xuefeng and Zhang, Jingfeng and Han, Bo and Liu, Tongliang and Rong, Yu and Niu, Gang and Huang, Junzhou and Sugiyama, Masashi},
  journal={ICML},
  year={2021}
}