PKU-ML/ReBAT

Official PyTorch implementation of ReBAT (ReBalanced Adversarial Training) in NeurIPS 2023 "Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective".

Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective

This is the official PyTorch implementation of the ReBAT (ReBalanced Adversarial Training) algorithm proposed in our NeurIPS 2023 paper Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective, by Yifei Wang*, Liangchen Li*, Jiansheng Yang, Zhouchen Lin and Yisen Wang.

Requirements

• Our code is compatiable with PyTorch 2.0.0.
• Please install AutoAttack from their official codebase.

Training

First, please run the following command to generate a validation set:

python3 generate_validation.py

Train a PreActResNet-18 model on CIFAR-10 with ReBAT:

CUDA_VISIBLE_DEVICES=0 python3 train_cifar_wa.py --val \
	--fname cifar10_res18 \
	--model PreActResNet18 \
	--chkpt-iters 10 \
	--lr-factor 1.5 \
	--beta 1.0

Train a PreActResNet-18 model on CIFAR-10 with ReBAT++:

CUDA_VISIBLE_DEVICES=0 python3 train_cifar_wa.py --val \
	--fname cifar10_res18_pp \
	--model PreActResNet18 \
	--chkpt-iters 10 \
	--lr-factor 1.7 \
	--beta 1.0 \
	--stronger-attack \
	--stronger-epsilon 10 \
	--stronger-attack-iters 12

Train a WideResNet-34-10 model on CIFAR-10 with ReBAT+CutMix:

CUDA_VISIBLE_DEVICES=0 python3 train_cifar_wa.py --val \
	--fname cifar10_wrn34_cutmix \
	--model WideResNet \
	--chkpt-iters 10 \
	--lr-factor 4.0 \
	--beta 2.0 \
	--cutmix

Evaluation

Evaluate a PreActResNet-18 model on CIFAR-10:

CUDA_VISIBLE_DEVICES=0 python3 train_cifar_wa.py --eval \
	--fname cifar10_res18 \
	--model PreActResNet18 \
	--resume 200

Evaluate a WideResNet-34-10 model on CIFAR-10:

CUDA_VISIBLE_DEVICES=0 python3 train_cifar_wa.py --eval \
	--fname cifar10_wrn34_cutmix \
	--model WideResNet \
	--resume 200

Checkpoint Release

We provide both best and last checkpoints under several experiment settings on Google Drive. Please download and place the subfolders under ./exps/ for evaluation.

Citation

Please cite our paper if you find our work useful.

@article{wang2023balance,
  title={Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective},
  author={Wang, Yifei and Li, Liangchen and Yang, Jiansheng and Lin, Zhouchen and Wang, Yisen},
  journal={Advances in neural information processing systems},
  volume={36},
  year={2023}
}