Official code for "Improving Few-Shot Learning through Multi-task Representation Learning Theory" ECCV 2022.
You can run pip install -r requirements.txt to install required packages or conda env create -f environment.yml to create a new environment with the required packages installed.
Train MAML with the script train_maml.sh. Add arguments --s_ratio and --s_norm to train with the regularization.
Train ProtoNet with the script train_proto.sh. Add arguments --norm to train with normalized prototypes.
Evaluate MAML or ProtoNet with the script eval.sh.
For Cross-dataset evaluation, change the argument --dataset. However, you will need to download and create your own Dataset class. In the folder datasets you can find the code for CropDisease, but you need to download the dataset manually.
This repository is released under the CeCILL license, a free software license adapted to both international and French legal matters that is fully compatible with the FSF's GNU/GPL license.
If you find this repository useful for your own work, please cite our paper:
@InProceedings{Bouniot2022improving,
author = {Bouniot, Quentin and Redko, Ievgen, and Audigier, Romaric and Loesch, Angélique and Habrard, Amaury},
title = {Improving Few-Shot Learning through Multi-task Representation Learning Theory},
booktitle = {European Conference on Computer Vision},
year = {2022}
}