CxGrad - Official PyTorch Implementation

Contextual Gradient Scaling for Few-Shot Learning
Sanghyuk Lee, Seunghyun Lee, and Byung Cheol Song
In WACV 2022.
Paper: https://arxiv.org/abs/2110.10353

Installation

This code is based on PyTorch. Please make a virtual environment and use it by running the command below:

conda env create --file environment.yml -n CxGrad
conda activate CxGrad

Datasets

We provide instructions to download 4 datasets: miniImageNet, tieredImageNet, CUB, and CIFAR-FS. Download the datasets you want to use and move them to datasets.

1. miniImageNet: Download mini_imagenet_full_size.tar.bz2 from this link, provided in MAML++. Note that by downloading and using the miniImageNet, you accept terms and conditions found in imagenet_license.md.

2. tieredImageNet: Download tiered_imagenet.tar from this link.

3. CIFAR-FS: Download cifar100.zip from this link. The splits and the download link are provided by Bertinetto.

4. CUB: Download CUB_200_2011.tgz from this link. The classes of each split are randomly chosen. Thus, we provide the splits of our experiments: CUB_split_train.txt, CUB_split_val.txt, and CUB_split_test.txt in datasets/preprocess. These splits are done by a script written by Chen.

Then, run the command below to preprocess the datasets you downloaded.

python preprocess/preprocess.py --datasets DATASET1 DATASET2 ...

The structure should be like this:

CxGrad 
  ├── datasets
  |      ├── miniImageNet
  |      |        ├── train
  |      |        ├── val
  |      |        └── test
  |      |── tieredImageNet
  |      |         ├── train
  |      |         ├── val
  |      |         └── test
  |      ├── CIFAR-FS
  |      |       ├── train
  |      |       ├── val
  |      |       └── test
  |      └── CUB
  |           ├── train
  |           ├── val
  |           └── test
  ├── utils
  ├── README.md
  └── ...

Run experiments

• Change directory to experiment_scripts.

Train

• In order to train the model on N-way K-shot miniImageNet classification, run

  bash mini_imagenet_Nway_Kshot/CxGrad_4conv.sh GPU_ID
  

• Otherwise for tieredImageNet, run

   bash tiered_imagenet_Nway_Kshot/CxGrad_4conv.sh GPU_ID
  

Test

• ex) Test on CUB using the model trained on 5-way 5-shot miniImageNet

   TEST=1 TEST_DATASET=CUB bash mini_imagenet_5way_5shot/CxGrad_4conv.sh GPU_ID
  

Citation

@inproceedings{lee2022contextual,
  title={Contextual Gradient Scaling for Few-Shot Learning},
  author={Lee, Sanghyuk and Lee, Seunghyun and Song, Byung Cheol},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={834--843},
  year={2022}
}

Acknowledgments

Thanks to the authors of MAML++ and ALFA, which our work is based on, for their great implementations.