Introduction

This is the code for the SDM 2023 Paper: Learning to Learn Task Transformations for Improved Few-Shot Classification.

Datasets

• miniImageNet

• CIFAR-FS

Change the values of the two parameters in config.py such that they point to the correct datasets.

Training Script

Metric-based meta-learning:

python main.py --head ProtoNet --network ProtoNet --dataset CIFAR_FS --temp 20.0 --step 3 --save-path ./l2tt_experiments --gpu 0 

• head could be R2D2, SVM (i.e., MetaOptNet in the paper), ProtoNet
• network could be ResNet, ProtoNet (i.e., CNN64 in the paper)
• step is the maximum policy length (corresponding to L in the paper)
• temp is the sampling temperature (corresponding to $\epsilon$ in the paper)
• dataset could be miniImageNet or CIFAR-FS

Gradient-based meta-learning

python train_maml.py --network ProtoNet --dataset CIFAR_FS --temp 20 --step 4 --save-path ./l2tt_experiments --gpu 0

• network could be ResNet, ProtoNet (i.e., CNN64 in the paper)
• MAML is supported

Citation

Please consider citing this paper if you find the code helpful.

@inproceedings{zhengSDM23learning,
  title={Learning to Learn Task Transformations for Improved Few-Shot Classification},
  author={Zheng, Guangtao and Suo, Qiuling and Huai, Mengdi and Zhang, Aidong},
  booktitle={SIAM International Conference on Data Mining (SDM)},
  year={2023}
}