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Robust Pre-Training by Adversarial Contrastive Learning

Introduction

Recent work has shown that, when integrated with adversarial training, self-supervised pre-training with several pretext tasks can lead to state-of-the-art robustness. In this work, we show that contrasting features to random and adversarial perturbations for consistency can benefit robustness-aware pre-training even further. Our approach leverages a recent contrastive learning framework, which learns representations by maximizing feature consistency under differently augmented views. This fits particularly well with the goal of adversarial robustness, as one cause of adversarial fragility is the lack of feature invariance, i.e., small input perturbations can result in undesirable large changes in features or even predicted labels. We explore various options to formulate the contrastive task, and demonstrate that by injecting adversarial augmentations, contrastive pre-training indeed contributes to learning data-efficient robust models. We extensively evaluate the proposed Adversarial Contrastive Learning (ACL) and show it can consistently outperform state-of-the-arts. For example on the CIFAR-10 dataset, ACL outperforms the latest unsupervised robust pre-training approach with substantial margins: 2.99% on robust accuracy and 2.14% on standard accuracy. We further demonstrate that ACL pre-training can improve semi-supervised adversarial training, even at very low label rates.

Method

pipeline Illustration of workflow comparison: (a) The original SimCLR framework, a.k.a., standard to standard (no adversarial attack involved); (b) - (d) three proposed variants of our adversarial contrastive learning framework: A2A, A2S, and DS (our best solution). Note that, whenever more than one encoder branches co-exist in one framework, they by default share all weights, except that adversarial and standard encoders will use independent BN parameters.

Environment requirements

Python (3.6.4)
Pytorch (1.5.0)
CUDA
numpy

Thanks to the question from Muhammad Zaid Hameed, we found that there would be a performance drop when using other Pytorch versions. Please use pytorch 1.5.0 or 1.5.1 for reproducing our results.

Pretraining

Pretrain the model on CIFAR-10 with ACL(DS)

python train_simCLR.py ACL_DS --ACL_DS --data /path/to/data

Pretrain the model on CIFAR-100 with ACL(DS)

python train_simCLR.py ACL_DS_CIFAR100 --ACL_DS --dataset cifar100 --data /path/to/data

Finetuning

Adversarial finetune ACL(DS) pretraining model on CIFAR-10 (Need to do ACL(DS) pretraining on CIFAR10 first)

python train_trades.py ACL_DS_TUNE --checkpoint checkpoints/ACL_DS/model_1000.pt --cvt_state_dict --bnNameCnt 1 --decreasing_lr 40,60 --epochs 100 --data /path/to/data

Adversarial finetune ACL(DS) pretraining model on CIFAR-100 (Need to do ACL(DS) pretraining on CIFAR100 first)

python train_trades.py ACL_DS_CIFAR100_TUNE --dataset cifar100 --checkpoint checkpoints/ACL_DS_CIFAR100/model_1000.pt --cvt_state_dict --bnNameCnt 1 --data /path/to/data

Semi-supervised adversarial training

On CIFAR-10 with 0.01 available labels (Need to do ACL(DS) pretraining on CIFAR10 first)

# train the standard model for generating psudo labels
python train_trades.py ACL_DS_SEMI_STAGE2_0.01LABELS --trainmode normal --trainset train0.01_idx --checkpoint checkpoints/ACL_DS/model_1000.pt --cvt_state_dict --bnNameCnt 0 --decreasing_lr 40,60 --epochs 100 --data /path/to/data
# Adversarial finetuning from ACL(DS) with the psudo labels
python train_trades_cifar10_semisupervised.py ACL_DS_SEMI_STAGE3_0.01LABELS --checkpoint checkpoints/ACL_DS/model_1000.pt --bnNameCnt 1 --cvt_state_dict --decreasing_lr 5,10 --epochs 15 --checkpoint_clean checkpoints_trade/ACL_DS_SEMI_STAGE2_0.01LABELS/best_model.pt --percentageLabeledData 1 --data /path/to/data

On CIFAR-10 with 0.1 available labels (Need to do ACL(DS) pretraining on CIFAR10 first)

# train the standard model for generating psudo labels
python train_trades.py ACL_DS_SEMI_STAGE2_0.1LABELS --trainmode normal --trainset train0.1_idx --checkpoint checkpoints/ACL_DS/model_1000.pt --cvt_state_dict --bnNameCnt 0 --decreasing_lr 40,60 --epochs 100 --data /path/to/data
# Adversarial finetuning from ACL(DS) with the psudo labels
python train_trades_cifar10_semisupervised.py ACL_DS_SEMI_STAGE3_0.1LABELS --checkpoint checkpoints/ACL_DS/model_1000.pt --bnNameCnt 1 --cvt_state_dict --decreasing_lr 5,10 --epochs 15 --checkpoint_clean checkpoints_trade/ACL_DS_SEMI_STAGE2_0.1LABELS/best_model.pt --percentageLabeledData 10 --data /path/to/data

Checkpoint

Pretraining

[ACL_DS]

Pretraining model on CIFAR10. To finetune from this checkpoint: use /path/to/downloadedCheckpoint for --checkpoint. For example

python train_trades.py ACL_DS_TUNE --checkpoint /path/to/downloadedCheckpoint --cvt_state_dict --bnNameCnt 1 --data /path/to/data

[ACL_DS_CIFAR100]

Pretraining model on CIFAR100. To finetune from this checkpoint: use /path/to/downloadedCheckpoint for --checkpoint. For example

CUDA_VISIBLE_DEVICES=0 python train_trades.py ACL_DS_TUNE_CIFAR100 --checkpoint checkpoints/ACL_DS_CIFAR100/ACL_DS_CIFAR100.pt --cvt_state_dict --bnNameCnt 1 --decreasing_lr 10,15 --epochs 20 --dataset cifar100

Finetuning

[ACL_DS_TUNE]

To evaluate:

python train_trades.py ACL_DS_TUNE --checkpoint /path/to/downloadedCheckpoint --eval-only --data /path/to/data

Semi-supervised adversarial training

[ACL_DS_SEMI_STAGE2_0.01LABELS]

To evaluate:

python train_trades.py ACL_DS_SEMI_STAGE2_0.01LABELS --checkpoint /path/to/downloadedCheckpoint --eval-only --data /path/to/data

[ACL_DS_SEMI_STAGE3_0.01LABELS]

To evaluate:

python train_trades.py ACL_DS_SEMI_STAGE3_0.01LABELS --checkpoint /path/to/downloadedCheckpoint --eval-only --data /path/to/data

[ACL_DS_SEMI_STAGE2_0.1LABELS]

To evaluate:

python train_trades.py ACL_DS_SEMI_STAGE2_0.1LABELS --checkpoint /path/to/downloadedCheckpoint --eval-only --data /path/to/data

[ACL_DS_SEMI_STAGE3_0.1LABELS]

To evaluate:

python train_trades.py ACL_DS_SEMI_STAGE3_0.1LABELS --checkpoint /path/to/downloadedCheckpoint --eval-only --data /path/to/data

Test against unforeseen attacks

Prepare the dataset CIFAR10-C

wget https://zenodo.org/record/2535967/files/CIFAR-10-C.tar?download=1 --output-document 'CIFAR-10-C.tar'
tar -xvf CIFAR-10-C.tar

Test against unforseen attacks with ACL_DS_TUNE (require ACL_DS_TUNE)

for atk in gaussian_noise impulse_noise pixelate shot_noise spatter zoom_blur motion_blur saturate snow speckle_noise contrast elastic_transform frost brightness defocus_blur fog gaussian_blur glass_blur jpeg_compression
do
CUDA_VISIBLE_DEVICES=0 python test2adversarial.py ACL_DS_TUNE --checkpoint /path/to/ACL_DS_TUNE --attack_type ${atk}
done

Citation

@article{ACL2020,
    title = {Robust Pre-Training by Adversarial Contrastive Learning},
    author = {Ziyu Jiang and Tianlong Chen and Ting Chen and Zhangyang Wang},
    booktitle = {Advances in Neural Information Processing Systems 34},
    year = {2020}
    }

Acknowledge

Trade fine-tuning code from TRADE (official code).