Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"
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
- 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
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}
}
The main structure of this code is based on MAML++. We thank the authors for sharing the codes for their great works.