vita-group/alleviate-robust-overfitting

[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang

Robust Overfitting may be mitigated by properly learned smoothening

Code for this paper Robust Overfitting may be mitigated by properly learned smoothing

Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang

Overview

To alleviate the intriguing problem of robust overfitting, we investigate two empirical means to inject more learned smoothening during adversarial training (AT): one leveraging knowledge distillation (KD) and self-training to smooth the logits, the other performing stochastic weight averaging (SWA) to smooth the weights

Highlights:

• Smoothening mitigates robust overfitting: After adopting KD and SWA in AT, we mitigated robust overfitting and achieve a better trade-off between standard test accuracy and robustness than early stopping.
• Rich ablation experiments: We conducted plenty of ablation experiments and visualizations to investigate the reason why robust overfitting may be mitigated by these smoothening approaches.

Experiment Results

Training with KD and SWA to mitigate robust overfitting

Flattening the rugged input space

Prerequisites

• pytorch 1.5.1
• torchvision 0.6.1
• advertorch 0.2.3

Usage

Standard Training:

python -u main_std.py \
	--data [dataset direction] \ 
	--dataset cifar10 \
	--arch resnet18 \
	--save_dir std_cifar10_resnet18 

PGD Adversarial Training:

python -u main_adv.py \
	--data [dataset direction] \ 
	--dataset cifar10 \
	--arch resnet18 \
	--save_dir AT_cifar10_resnet18 

Adversarial Training with KD&SWA:

python -u main_adv.py \
	--data [dataset direction] \ 
	--dataset cifar10 \
	--arch resnet18 \
	--save_dir KDSWA_cifar10_resnet18 \
	--swa \
	--lwf \
	--t_weight1 pretrained_models/cifar10_resnet18_std_SA_best.pt \
	--t_weight2 pretrained_models/cifar10_resnet18_adv_RA_best.pt

Testing under PGD-20 Linf eps=8/255 :

python -u main_adv.py \
	--data [dataset direction] \
	--dataset cifar10 \
	--arch resnet18 \
	--eval \
	--pretrained pretrained_models/**.pt \
	--swa #if test with swa_model

Citation

@inproceedings{
	chen2021robust,
	title={Robust Overfitting may be mitigated by properly learned smoothening},
	author={Tianlong Chen and Zhenyu Zhang and Sijia Liu and Shiyu Chang and Zhangyang Wang},
	booktitle={International Conference on Learning Representations},
	year={2021},
	url={https://openreview.net/forum?id=qZzy5urZw9}
}

alleviate-robust-overfitting's Issues

Pretrained models

Hi, where are the pretrained models? Can you provide?

--t_weight1 pretrained_models/cifar10_resnet18_std_SA_best.pt
--t_weight2 pretrained_models/cifar10_resnet18_adv_RA_best.pt