FS-BAN: Born-Again Networks for
Domain Generalization Few-Shot Classification

Yunqing Zhao ,  Ngai‑Man Cheung

Singapore University of Technology and Design
IEEE Transactions on Image Processing (T-IP), 2023

[Project Page]  /  [Paper Profile]  /  [Data Repository]

Pytorch implementation for our FS-BAN for cross-domain / domain generalization few-shot classification. With the proposed born-again networks with multi-task learning, we are able to:

1. improve exisiting few-shot classification methods under cross-domain setting to stat-of-the-art performance
2. achieve stat-of-the-art performance under single-domain few-shot classification setting.

Installation:

• Platform: Linux
• NVIDIA V100 GPUs with CuDNN 10.1
• PyTorch>=1.4.0
• lmdb, tqdm, wandb

Firstly, clone this repository:

git clone https://github.com/yunqing-me/Born-Again-FS.git
cd Born-Again-FS

You can install the libiraries through: pip install -r requirements.txt. Alternatively, a suitable conda environment named fsc can be created and activated with:

conda env create -f environment.yml -n fsc
conda activate fsc

Datasets

Download 5 datasets seperately with the following commands. Set DATASET_NAME to either: cars, cub, miniImagenet, places, or plantae.

cd filelists
python process.py DATASET_NAME
cd ..

You may encounter some download issues while processing these datasets, this is due to the original dataset links were invalid. Here, we provide the data repository to help download those datasets. Then, simply move each separate dataset e.g., CUB_200_2011.tgz to the corresponding folder e.g., ./filelists/cub and use the script to process it e.g., write_cub_filelist.py.

Meanwhile, to download and process tieredImageNet dataset, please refer to Torchmeta.

Experiments

Feature Encoder Pretraining

we pre-train the model using a linear classifier head on training set of mini-ImageNet (64 categories).

Similar to CrossDomainFewShot, We adopt baseline++ for MatchingNet, and baseline from CloserLookFewShot for other metric-based frameworks.

python train_baseline.py --method PRETRAIN --dataset miniImagenet --name PRETRAIN --train_aug

You can specify --train_aug to perform data augmentation, --method baseline or --method baseline++ to decide the metric of the classifier. After pretraining, we replace the linear classifier head with different metric functions.

Alternatively, you can directly download the pretrained encoder (provieded by CrossDomainFewShot):

cd output/checkpoints
python download_encoder.py
cd ../..

Training the Teacher Network

cd baseline_model
bash _train_teacher.sh

where you can specify the model architecutre in Conv4/Conv6/Resnet10 etc. Meanwhile, it is necessary to prepare the corresponding (pretrained) models for --warmup to load the pretrained weights. For each individual datasets, you need to prepare the corresponding teacher network, by specifying --dataset.

FS-BAN: Born-Again Distillation for DG-FSC

cd fsban
bash _train_student.sh

where you can tune the hyperparameters in the script.

Evaluation:

Test the metric-based framework METHOD on the unseen domain TESTSET (held-out from muliple seen source domains).

Specify the saved model (in ./output/checkpoints) you want to evaluate with --name (e.g., --name YOUR-Model-Name from the above example).

python test.py --method METHOD --name NAME --dataset TESTSET

Bibtex

If you find this project useful in your research, please consider citing our paper:

@article{zhao2023fs,
  title={Fs-ban: Born-again networks for domain generalization few-shot classification},
  author={Zhao, Yunqing and Cheung, Ngai-Man},
  journal={IEEE Transactions on Image Processing},
  year={2023},
  publisher={IEEE}
}

Acknowledgement

We appreciate the wonderful base implementation of Cross-domain Few-shot Classification from @Hung-Yu Tseng.

We especially thank for the fruitful discussion with Yiluan Guo (Motional), Jiamei Sun (Microsoft) and Milad Abdollahzadeh (SUTD).