Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. pdf
https://github.com/csyanbin/TPN-pytorch
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
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
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
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
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
If you use our code, please consider to cite the following paper:
@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},
}