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

Related tags

Deep Learning machine-learning reinforcement-learning computer-vision deep-learning pytorch maml meta-learning few-shot-learning cvpr2020
Overview

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.

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Comments
  • Figure 2 플롯 방법

    Figure 2 플롯 방법

    안녕하세요. 먼저 코드를 공유해주셔서 감사합니다. 논문을 보면 [26] 논문에서 보여준 것처럼 Figure 2에서 loss landscape과 gradient predictiveness, 그리고 ꞵ-smoothness에 대한 plot을 확인할 수가 있는데요. 해당 plot들을 어떤 방식으로 구현을 하셨는지 여쭙고 싶습니다.

    1. 각각의 inner-loop step에서 구한 gradient 방향에 대해 learning rate를 조정하여 여러 weight를 구한 다음 각각에 대한 loss 및 gradient를 구해서 plot하는게 맞나요?
    2. Appendix A에서 'We take an average of these values over the number of inner-loop updates and plot them against training iterations'로 표현을 하셨는데, 이는 1에서 조정된 여러 learning rate에 대해 inner-loop step (코드에서는 5회)에 대해 averaging한 것으로 이해하면 될까요?
    3. 1번의 방식이 맞다면, learning rate 조정의 범위는 어떻게 설정하셨는지 여쭙고 싶습니다. [26] 논문을 보면 Appendix A.2.2에서 [1/2, 4] X step size로 스텝 길이를 조정했다고 나와 있는데, 해당 범위 내에서도 선택할 수 있는 값들이 많기 때문에 어떠한 방식으로 진행하셨는지 궁금합니다.

    건강 조심하세요. 감사합니다.

    opened by shlee625 5
  • The implement of Resnet12 backbone

    The implement of Resnet12 backbone

    Hi, thank you for your implement L2F in pytorch.

    But I found the implement is based on the conv4 backbone，could you release the implement of Resnet12 backbone?

    opened by sunhm15 2
  • About LEO and other experiment implement details (like ablation study, conflicts study)

    About LEO and other experiment implement details (like ablation study, conflicts study)

    Hi David! I really appreciate what you did in this work, I believe this should be considered as a great progress in meta-learning. I check your repository and find that the leo experiment and conflicts study mentioned in paper didn't show up here, would you mind adding them to this repository? That would be really helpful, thanks a lot.

    p.s. salute to your hard work with only one 1080ti in all these experiments:)

    opened by ligeng0197 1
Owner
Sungyong Baik
Ph.D. Student in CVLab, SNU
Sungyong Baik
GitHub https://github.com/baiksung/L2F
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