https://github.com/csyanbin/TPN

Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
https://github.com/csyanbin/TPN

Last synced: 13 days ago
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Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.

• Host: GitHub
• URL: https://github.com/csyanbin/TPN
• Owner: csyanbin
• Created: 2018-12-22T10:26:19.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-04T10:09:19.000Z (over 5 years ago)
• Last Synced: 2024-07-31T23:45:08.141Z (3 months ago)
• Language: Python
• Homepage:
• Size: 3.2 MB
• Stars: 242
• Watchers: 6
• Forks: 43
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-few-shot-meta-learning - code - official (TF)

README

        
# Transductive Propagation Network

Code for ICLR19 paper: 

*Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.* [pdf](https://openreview.net/pdf?id=SyVuRiC5K7)

## Pytorch Version 

https://github.com/csyanbin/TPN-pytorch

## Requirements

* Python 3.5

* Tensorflow 1.3+

* tqdm

## Data Download (miniImagenet and tieredImagenet)

Please download the compressed tar files from: https://github.com/renmengye/few-shot-ssl-public

```

mkdir -p data/miniImagenet/data

tar -zxvf mini-imagenet.tar.gz

mv *.pkl data/miniImagenet/data

mkdir -p data/tieredImagenet/data

tar -xvf tiered-imagenet.tar

mv *.pkl data/tieredImagenet/data

```

## TPN mini-5way1shot

```

python train.py --gpu=0 --n_way=5 --n_shot=1 --n_test_way=5 --n_test_shot=1 --lr=0.001 --step_size=10000 --dataset=mini --exp_name=mini_TPN_5w1s_5tw1ts_rn300_k20 --rn=300 --alpha=0.99 --k=20

```

```

python test.py --gpu=0 --n_way=5 --n_shot=1 --n_test_way=5 --n_test_shot=1 --lr=0.001 --step_size=10000 --dataset=mini --exp_name=mini_TPN_5w1s_5tw1ts_rn300_k20 --rn=300 --alpha=0.99 --k=20 --iters=81500

```

## TPN mini-5way5shot

```

python train.py --gpu=0 --n_way=5 --n_shot=5 --n_test_way=5 --n_test_shot=5 --lr=0.001 --step_size=10000 --dataset=mini --exp_name=mini_TPN_5w5s_5tw5ts_rn300_k20 --rn=300 --alpha=0.99 --k=20

```

```

python test.py --gpu=0 --n_way=5 --n_shot=5 --n_test_way=5 --n_test_shot=5 --lr=0.001 --step_size=10000 --dataset=mini --exp_name=mini_TPN_5w5s_5tw5ts_rn300_k20 --rn=300 --alpha=0.99 --k=20 --iters=50100

```

## TPN tiered-5way1shot

```

python train.py --gpu=0 --n_way=5 --n_shot=1 --n_test_way=5 --n_test_shot=1 --lr=0.001 --step_size=25000 --dataset=tiered --exp_name=tiered_TPN_5w1s_5tw1ts_rn300_k20 --rn=300 --alpha=0.99 --k=20

```

## TPN tiered-5way5shot

```

python train.py --gpu=0 --n_way=5 --n_shot=5 --n_test_way=5 --n_test_shot=5 --lr=0.001 --step_size=25000 --dataset=tiered --exp_name=tiered_TPN_5w5s_5tw5ts_rn300_k20 --rn=300 --alpha=0.99 --k=20

```

## Citation

If you use our code, please consider to cite the following paper:

* Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sungju Hwang, Yi Yang. Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. In *Proceedings of 7th International Conference on Learning Representations (ICLR)*, 2019.

```

@inproceedings{liu2019fewTPN,

	title={Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning},

	author={Yanbin Liu and 

		Juho Lee and 

		Minseop Park and 

		Saehoon Kim and 

		Eunho Yang and 

		Sungju Hwang and 

		Yi Yang},

booktitle={International Conference on Learning Representations},

year={2019},

}

```