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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.
Model Backbone Batch Size 1-shot Accuracy 5-shot Accuracy
MAML ResNet12 1 51.03±0.50% 68.26±0.47%
MAML+L2F ResNet12 1 57.48±0.49% 74.68±0.43%
MAML ResNet12 2 58.37±0.49% 69.76±0.46%
MAML+L2F ResNet12 2 59.71±0.49% 77.04±0.42%
  • 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.