1 StyleAdv-CDFSL

Repository for the CVPR-2023 paper : StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

[Paper], [Presentation Video on Bilibili], [Presentation Video on Youtube]

2 Setup

2.1 conda env & code

# conda env
conda create --name py36 python=3.6
conda activate py36
conda install pytorch torchvision -c pytorch
conda install pandas
pip3 install scipy>=1.3.2
pip3 install tensorboardX>=1.4
pip3 install h5py>=2.9.0
pip3 install tensorboard
pip3 install timm
pip3 install opencv-python==4.5.5.62
pip3 install ml-collections
# code
git clone https://github.com/lovelyqian/StyleAdv-CDFSL
cd StyleAdv-CDFSL

2.2 datasets

We use the mini-Imagenet as the single source dataset, and use cub, cars, places, plantae, ChestX, ISIC, EuroSAT, and CropDisease as novel target datasets.

For the mini-Imagenet, cub, cars, places, and plantae, we refer to the FWT repo.

For the ChestX, ISIC, EuroSAT, and CropDisease, we refer to the BS-CDFSL repo.

If you can't find the Plantae dataset, we provide at here, PIN: 0000, please cite its paper.

3 StyleAdv based on ResNet

3.1 meta-train StyleAdv

Our method aims at improving the generalization ability of models, we apply the style attack and adversarial training during the meta-train stage. Once the model is meta-trained, it can be used for inference on different novel target datasets directly.

Taking 5-way 1-shot as an example, the meta-train can be done as,

python3 metatrain_StyleAdv_RN.py --dataset miniImagenet --name exp-name --train_aug --warmup baseline --n_shot 1 --stop_epoch 200

• We integrate the testing into the training, and the testing results can be found on output/checkpoints/exp-name/acc*.txt;

• We set a probability $p_{skip}$ for randomly skipping the attack, the value of it can be modified in methods/tool_func.py;

• We also provide our meta-trained ckps in output/checkpoints/StyleAdv-RN-1shot and output/checkpoints/StyleAdv-RN-5shot;

3.2 fine-tune the meta-trained StyleAdv

Though not necessary, for better performance, you may further fine-tune the meta-trained models on the target sets.

Taking 5-way 1-shot as an example, the fine-tuning on cars can be done as,

python3 finetune_StyleAdv_RN.py --testset cars --name exp-FT --train_aug --n_shot 1 --finetune_epoch 10 --resume_dir StyleAdv-RN-1shot --resume_epoch -1

• The finetuning is very sensitive to the fintune_epoch and finetune_lr;

• The value of finetune_lr can be modified in finetune_StyleAdv_RN.py :(sorry for not organizing the code very well;

• As attached in the supplementary materials of paper, we set the finetune_epoch and finetune_lr as:

  Backbone	Task	Optimizer	finetune_epoch	finetune_lr
  RN10	5-way 5-shot	Adam	50	{0,0.001}
  RN10	5-way 1-shot	Adam	10	{0,0.005}

4 StyleAdv based on ViT

coming soon

5 Citing

If you find our paper or this code useful for your research, please considering cite us (●°u°●)」:

@inproceedings{fu2023styleadv,
  title={StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning},
  author={Fu, Yuqian and Xie, Yu and Fu, Yanwei and Jiang, Yu-Gang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24575--24584},
  year={2023}
}

6 Acknowledge

Our code is built upon the implementation of FWT, ATA, and PMF. Thanks for their work.