Cooperative Meta-Learning with Gradient Augmentation (CML)

This repository provides the code for the UAI 2024 (Main Track) paper titled Cooperative Meta-Learning with Gradient Augmentation (CML).

Overview

Requirements

• Python >= 3.9
• Pytorch == 1.12

pip install -r requirements.txt

Training & Evaluation

To train and evalute the 4-conv model with CML in the paper, run this command:

./run_cml.sh

If you want to train standard MAML, run this command:

./run_maml.sh

Train

You can use train_cml.py to meta-train your model in CML framework. For example, to run Miniimagenet 5-way 5-shot, run this command:

train_cml.py --folder=~/data --dataset=miniimagenet --model=4-conv_cml --num-ways=5  --num-shots=5  --extractor-step-size=0.5  --classifier-step-size=0.5 --loss-scaling=1 --output-folder=./result --save-name=CML

• You can download the dataset from option --download

Evaluation

You can use test_cml.py to meta-test your model in CML framework. run this command:

test_cml.py --folder=~/data --dataset=miniimagenet --model=4-conv_cml --num-ways=5  --num-shots=5 --extractor-step-size=0.5 --classifier-step-size=0.5 --output-folder=./result --save-name=CML --use-colearner

• If you want to test co-learner, you can use the option --use-colearner.
• Note that it must be the same as the path to the model saved by train (ouput-folder, save-name)

References

This code is based on the implementations of BOIL.

Citation

If you use this project in your research, please cite our paper:

@article{shin2024cooperative,
  title={Cooperative Meta-Learning with Gradient Augmentation},
  author={Jongyun Shin and Seunjin Han and Jangho Kim},
  journal={arXiv preprint arXiv:2406.04639},
  year={2024}
}