This is the official code for IJCAI 2019 Paper: Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

We find weakly-labeled data as well as the propagation mechanism improve the performance of few-shot learning a lot.

If you find this project helpful, please consider to cite the following paper:

@inproceedings{liu2019ppn,
title={Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph},
author={Liu, Lu and Zhou, Tianyi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi},
booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
year={2019}
}

• Python 3.6
• Pytorch 1.0.0

• Download datasets from Google Drive
• Enter the dir of the downloaded datasets. Extract the datasets by tar -xvf tiered-imagenet-pure.tar --directory ~/datasets/ or tar -xvf tiered-imagenet-mix.tar --directory ~/datasets/

• For 5 way 1 shot experiment on tiered-imagenet-pure: bash scripts/pp_buffer/train_anc.sh 0 5 1 pure all_level_avg_single 1, where in order 0 is for which GPU to use, 5 is way, 1 is shot, pure is dataset (mix otherwise), all_level_avg_single is training strategy used in our paper, 1 is the number of hops for propagation.

• For 5 way 5 shot experiment on tiered-imagenet-mix, where each parameter follows the same setup: bash scripts/pp_buffer/train_base_anc.sh 0 5 1 mix all_level_avg_single 1

• For 5 way 1 shot experiment on tiered-imagenet-pure: bash scripts/pp_buffer/test_anc_all_level.sh 0 5 1 pure all_level_avg_single 1 SEED, where SEED is the random seed used in training (check the name of the training logs) and the other parameters follow the same setup.

• For 5 way 5 shot experiment on tiered-imagenet-pure, where each parameter follows the same setup: bash scripts/pp_buffer/test_base_anc_all_level.sh 0 5 1 mix all_level_avg_single 1 SEED