Adversarial Contrastive Learning via Asymmetric InfoNCE

This is a PyTorch implementation of the paper Adversarial Contrastive Learning via Asymmetric InfoNCE (ECCV 2022).

Preparation

In each sub-directory (./Asym-AdvCL, ./Asym-RoCL) for each baseline, please first create ./logs directories, then download CIFAR datasets into ～/data. All models will be stored in ./checkpoint, and logs stored in ./logs.

Environment requirements

• PyTorch >= 1.8
• NVIDIA apex
• numpy
• tensorboard_logger
• torchlars == 0.1.2
• pytorch-gradual-warmup-lr
• diffdist == 0.1

This repo is a modification on the RoCL and AdvCL repos. Environments can also be installed according to the requirements of RoCL and AdvCL for experiments of each baseline.

Training

We provide shells for reproducing our main results in Table 1. The hyperparameter settings of baselines are the same as their original papers reported. All experiments were conducted on 2 Tesla V100 GPUs.

First, for RoCL:

• RoCL on CIFAR10

sh shell/rocl-cifar10.sh rocl-cifar10 ResNet18 cifar-10 | tee logs/rocl-cifar10.log 

• RoCL-IP (RoCL with inferior positive) on CIFAR10

sh shell/rocl-IP-cifar10.sh rocl-IP-cifar10 ResNet18 cifar-10 | tee logs/rocl-IP-cifar10.log 

• RoCL-HN (RoCL with hard negative) on CIFAR10

sh shell/rocl-HN-cifar10.sh rocl-HN-cifar10 ResNet18 cifar-10 | tee logs/rocl-HN-cifar10.log 

• RoCL-IPHN (RoCL with both) on CIFAR10

sh shell/rocl-IPHN-cifar10.sh rocl-IPHN-cifar10 ResNet18 cifar-10 | tee logs/rocl-IPHN-cifar10.log 

• RoCL on CIFAR100

sh shell/rocl-cifar100.sh rocl-cifar100 ResNet18 cifar-100 | tee logs/rocl-cifar100.log 

• RoCL-IP on CIFAR100

sh shell/rocl-IP-cifar100.sh rocl-IP-cifar100 ResNet18 cifar-100 | tee logs/rocl-IP-cifar100.log 

• RoCL-HN on CIFAR100

sh shell/rocl-HN-cifar100.sh rocl-HN-cifar100 ResNet18 cifar-100 | tee logs/rocl-HN-cifar100.log

• RoCL-IPHN on CIFAR100

sh shell/rocl-IPHN-cifar100.sh rocl-IPHN-cifar100 ResNet18 cifar-100 | tee logs/rocl-IPHN-cifar100.log 

For AdvCL:

• AdvCL on CIFAR10

sh shell/cifar10/advcl-cifar10.sh advcl-cifar10 | tee logs/advcl-cifar10.log

• AdvCL-IP on CIFAR10

sh shell/cifar10/advcl-IP-cifar10.sh advcl-IP-cifar10 | tee logs/advcl-IP-cifar10.log

• AdvCL-HN on CIFAR10

sh shell/cifar10/advcl-HN-cifar10.sh advcl-HN-cifar10 | tee logs/advcl-HN-cifar10.log

• AdvCL-IPHN on CIFAR10

sh shell/cifar10/advcl-IPHN-cifar10.sh advcl-IPHN-cifar10 | tee logs/advcl-IPHN-cifar10.log

• AdvCL on CIFAR100

sh shell/cifar100/advcl-cifar100.sh advcl-cifar100 | tee logs/advcl-cifar100.log

• AdvCL-IP on CIFAR100

sh shell/cifar100/advcl-IP-cifar100.sh advcl-IP-cifar100 | tee logs/advcl-IP-cifar100.log

• AdvCL-HN on CIFAR100

sh shell/cifar100/advcl-HN-cifar100.sh advcl-HN-cifar100 | tee logs/advcl-HN-cifar100.log

• AdvCL-IPHN on CIFAR100

sh shell/cifar100/advcl-IPHN-cifar100.sh advcl-IPHN-cifar100 | tee logs/advcl-IPHN-cifar100.log

Citation

If you find our work useful or provides some new insights about adversarial contrastive learning:blush:, please consider citing:

@inproceedings{yu2022adversarial,
  title={Adversarial Contrastive Learning via Asymmetric InfoNCE},
  author={Yu, Qiying and Lou, Jieming and Zhan, Xianyuan and Li, Qizhang and Zuo, Wangmeng and Liu, Yang and Liu, Jingjing},
  booktitle={European Conference on Computer Vision},
  pages={53--69},
  year={2022},
  organization={Springer}
}

Acknowledgements

We thank for the code implementation from RoCL, AdvCL, HCL and SupContrast.