Code release for the paper: "TLRM: Task-level Relation Module for GNN-based Few-Shot Learning" (IEEE VCIP 2021)

Platform

This code was developed and tested with pytorch version 1.0.1

Training

# ************************** miniImagenet, 5way 1shot *****************************
$ python3 train.py --dataset mini --num_ways 5 --num_shots 1 --transductive False
$ python3 train.py --dataset mini --num_ways 5 --num_shots 1 --transductive True

# ************************** miniImagenet, 5way 5shot *****************************
$ python3 train.py --dataset mini --num_ways 5 --num_shots 5 --transductive False
$ python3 train.py --dataset mini --num_ways 5 --num_shots 5 --transductive True


# **************** miniImagenet, 5way 5shot, 20% labeled (semi) *********************
$ python3 train.py --dataset mini --num_ways 5 --num_shots 5 --num_unlabeled 4 --transductive False
$ python3 train.py --dataset mini --num_ways 5 --num_shots 5 --num_unlabeled 4 --transductive True

Evaluation

$ python3 eval.py --test_model D-mini_N-5_K-1_U-0_L-3_B-40_T-True

References

Thanks for your attention!

Our code is based on Kim's contribution. Specifically, except for our core design Task-level Relation Module, everything else （e.g. backbone, dataset, EGNN, evaluation standards, hyper-parameters）are built on and integrated in https://github.com/khy0809/fewshot-egnn.

If you have any suggestion or question, you can leave a message here or contact us directly:

• guoyurong@bupt.edu.cn
• mazhanyu@bupt.edu.cn