L2F - Learning to Forget for Meta-Learning

Sungyong Baik, Seokil Hong, Kyoung Mu Lee

Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

Paper

Proposed Meta-Learning

Dataset Preparation

The miniImageNet dataset can be downloaded from the link provided in MAML++ github page.

Once downloaded, place it in the datasets folder.

Note: By downloading and using the miniImageNet datasets, you accept terms and conditions found in imagenet_license.md

Results

• Note that the reported results for ResNet12 were trained with batch size of 1 to fit into 11GB GPU Memory.
• With more than 22GB memory, models with ResNet12 backbone can be trained with batch size of 2 (the usual setting for 5-way 5-shot classification) to get higher accuracy.

• 5-way classification results on miniImageNet

Citation

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{baik2020learning,
    author = {Baik, Sungyong and Hong, Seokil and Lee, Kyoung Mu},
    title = {Learning to Forget for Meta-Learning},
    booktitle = {CVPR},
    year = {2020}
}

Acknowledgement

The main structure of this code is based on MAML++. We thank the authors for sharing the codes for their great works.