ADM in PyTorch

We provide a PyTorch implementation of ADM for few-shot learning. If you use this code for your research, please cite our paper.

@inproceedings{li2020ADM,
  title={Asymmetric Distribution Measure for Few-shot Learning},
  author={Li, Wenbin and Wang, Lei and Huo, Jing and Shi, Yinghuan and Gao, Yang and Luo, Jiebo},
  booktitle={IJCAI},
  year={2020}
}

Asymmetric Distribution Measure for Few-shot Learning.
Wenbin Li, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao and Jiebo Luo. In IJCAI 2020.

Prerequisites

• Linux
• Python 3.5
• Pytorch 1.3
• GPU + CUDA CuDNN
• pillow, torchvision, scipy, numpy

Getting Started

Installation

• Clone this repo:

  git clone https://github.com/WenbinLee/ADM.git
  cd ADM

• Install PyTorch 1.3 and other dependencies.

Datasets

• miniImageNet.
• tieredImageNet.
  Note that all the images need to be stored into a file named "images", and the data splits are stored into "train.csv", "val.csv" and "test.csv", respectively.

miniImageNet Few-shot Classification with Data Augmentation

• Train a 5-way 1-shot model based on Conv64F:

  python Train_Batch_miniImageNet.py --dataset_dir ./datasets/miniImageNet --method_name KL --way_num 5 --shot_num 1

• Test the model (specify the dataset_dir first):

  python Test_Batch.py --resume ./results/miniImageNet_DA/KL_BatchSize_4_Conv64F_miniImageNet_5Way_1Shot/model_best.pth.tar --data_name miniImageNet --method_name KL --way_num 5 --shot_num 1

tieredImageNet Few-shot Classification without Data Augmentation

• Train a 5-way 1-shot model based on Conv64F:

  python Train_Batch_miniImageNet.py --dataset_dir ./datasets/tieredImageNet --method_name KL --way_num 5 --shot_num 1

• Test the model (specify the dataset_dir first):

  python Test_Batch.py --resume ./results/tieredImageNet_NoDA/KL_BatchSize_4_Conv64F_tieredImageNet_5Way_1Shot/model_best.pth.tar --data_name miniImageNet --method_name KL --way_num 5 --shot_num 1

• The results on the miniImageNet and tieredImageNet datasets:

  

Citation

If you use this code for your research, please cite our paper.

@inproceedings{li2020ADM,
  title={Asymmetric Distribution Measure for Few-shot Learning},
  author={Li, Wenbin and Wang, Lei and Huo, Jing and Shi, Yinghuan and Gao, Yang and Luo, Jiebo},
  booktitle={IJCAI},
  year={2020}
}