[Project Page][Paper]

Pytorch implementation for our cross-domain few-shot classification method. With the proposed learned feature-wise transformation layers, we are able to:

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

Contact: Hung-Yu Tseng ([email protected])

Please cite our paper if you find the code or dataset useful for your research.

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Ming-Hsuan Yang
International Conference on Learning Representations (ICLR), 2020 (spotlight)

@inproceedings{crossdomainfewshot,
  author = {Tseng, Hung-Yu and Lee, Hsin-Ying and Huang, Jia-Bin and Yang, Ming-Hsuan},
  booktitle = {International Conference on Learning Representations},
  title = {Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation},
  year = {2020}
}

• Python >= 3.5
• Pytorch >= 1.3 and torchvision (https://pytorch.org/)
• You can use the requirements.txt file we provide to setup the environment via Anaconda.

conda create --name py36 python=3.6
conda install pytorch torchvision -c pytorch
pip3 install -r requirements.txt

Clone this repository:

git clone https://github.com/hytseng0509/CrossDomainFewShot.git
cd CrossDomainFewShot

Download 5 datasets seperately with the following commands.

• Set DATASET_NAME to: cars, cub, miniImagenet, places, or plantae.

cd filelists
python3 process.py DATASET_NAME
cd ..

• Refer to the instruction here for constructing your own dataset.

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

• Download the pre-trained feature encoders.

cd output/checkpoints
python3 download_encoder.py
cd ../..

• Or train your own pre-trained feature encoder (specify PRETRAIN to baseline++ or baseline).

python3 train_baseline.py --method PRETRAIN --dataset miniImagenet --name PRETRAIN --train_aug

Baseline training w/o feature-wise transformations.

• METHOD : metric-based framework matchingnet, relationnet_softmax, or gnnnet.
• TESTSET: unseen domain cars, cub, places, or plantae.

python3 train_baseline.py --method METHOD --dataset multi --testset TESTSET --name multi_TESTSET_ori_METHOD --warmup PRETRAIN --train_aug

Training w/ learning-to-learned feature-wise transformations.

python3 train.py --method METHOD --dataset multi --testset TESTSET --name multi_TESTSET_lft_METHOD --warmup PRETRAIN --train_aug

Test the metric-based framework METHOD on the unseen domain TESTSET.

• Specify the saved model you want to evaluate with --name (e.g., --name multi_TESTSET_lft_METHOD from the above example).

python3 test.py --method METHOD --name NAME --dataset TESTSET

• This code is built upon the implementation from CloserLookFewShot.
• The dataset, model, and code are for non-commercial research purposes only.
• You can change the number of shot (i.e. 1/5 shots) using the argument --n_shot.
• You need a GPU with 16G memory for training the gnnnet approach w/ learning-to-learned feature-wise transformations.
• We are still verifying the code of training with multiple seen domains.