Adversarial Weight Perturbation Helps Robust Generalization
Code for NeurIPS 2020 "Adversarial Weight Perturbation Helps Robust Generalization" by Dongxian Wu, Shu-Tao Xia, and Yisen Wang.
News
10/13/2020 - Our code and paper are released.
Requisite
This code is implemented in PyTorch, and we have tested the code under the following environment settings:
- python = 3.7.3
- torch = 1.2.0
- torchvision = 0.4.0
What is in this repository
Codes for our AWP-based adversarial training (AT-AWP) are in at-awp, and those for AWP-based TRADES (TRADES-AWP) are in ./trades-awp:
- In
./at-awp, the codes for CIFAR-10, CIFAR-100, and SVHN are intrain_cifar10.py,train_cifar100.py,train_svhn.pyrespectively. - In
./trades-awp, the codes for CIFAR-10 and CIFAR-100 are intrain_trades_cifar.py.
The checkpoints can be found in Google Drive or Baidu Drive(pw: 8tsv).
How to use it
For AT-AWP with a PreAct ResNet-18 on CIFAR-10 under L_inf threat model (8/255), run codes as follows,
python train_cifar10.py --data-dir DATASET_DIR
where $DATASET_DIR is the path to the dataset.
For TRADES-AWP with a WRN-34-10 on CIFAR10 under L_inf threat model (8/255), run codes as follows,
python train_trades_cifar.py --data CIFAR10 --data-path DATASET_DIR
The Leaderboard Under Auto Attack
To verify the effectiveness of AWP further, we evaluate the robustness under a stronger attack, auto-attack [3]. Here we only list Top 10 results on the leadboard (up to 10/13/2020) and our results. Compared with the leadboard results, AWP can boost the robustness of the AT and its variants (TRADES[2], MART[4], Pre-training[5], RST[6], etc.), ranking 1st on both with and without data. Even some AWP-based methods without additional data can surpass the results under additional data.
More results can be found in ./auto-attacks
| # | method / paper | model | architecture | clean | report. | AA |
|---|---|---|---|---|---|---|
| - | RST-AWP (ours)‡ | downloads | WRN-28-10 | 88.25 | - | 60.04 |
| 1 | (Wu et al., 2020)‡ | available | WRN-34-15 | 85.60 | 59.78 | 59.78 |
| 2 | (Carmon et al., 2019) RST‡ | available | WRN-28-10 | 89.69 | 62.5 | 59.53 |
| - | Pre-training-AWP (ours)‡ | downloads | WRN-28-10 | 88.33 | - | 57.39 |
| 3 | (Sehwag et al., 2020)‡ | available | WRN-28-10 | 88.98 | - | 57.14 |
| 4 | (Wang et al., 2020)‡ | available | WRN-28-10 | 87.50 | 65.04 | 56.29 |
| - | TRADES-AWP (ours) | downloads | WRN-34-10 | 85.36 | - | 56.17 |
| 5 | (Alayrac et al., 2019)‡ | available | WRN-106-8 | 86.46 | 56.30 | 56.03 |
| 6 | (Hendrycks et al., 2019) Pre-training‡ | available | WRN-28-10 | 87.11 | 57.4 | 54.92 |
| - | MART-AWP (ours) | downloads | WRN-34-10 | 84.43 | - | 54.23 |
| - | AT-AWP (ours) | downloads | WRN-34-10 | 85.36 | - | 53.97 |
| 7 | (Pang et al., 2020b) | available | WRN-34-20 | 85.14 | - | 53.74 |
| 8 | (Zhang et al., 2020b) | available | WRN-34-10 | 84.52 | 54.36 | 53.51 |
| 9 | (Rice et al., 2020) AT | available | WRN-34-20 | 85.34 | 58 | 53.42 |
| 10 | (Huang et al., 2020)* | available | WRN-34-10 | 83.48 | 58.03 | 53.34 |
Citing this work
@inproceedings{wu2020adversarial,
title={Adversarial Weight Perturbation Helps Robust Generalization},
author={Dongxian Wu and Shu-Tao Xia and Yisen Wang},
booktitle={NeurIPS},
year={2020}
}
Reference Code
[1] AT: https://github.com/locuslab/robust_overfitting
[2] TRADES: https://github.com/yaodongyu/TRADES/
[3] AutoAttack: https://github.com/fra31/auto-attack
[4] MART: https://github.com/YisenWang/MART
[5] Pre-training: https://github.com/hendrycks/pre-training