This repository provides the algorithm demonstration for the paper A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks.
Install dependencies (I prefer a conda environment)
conda create -n MLL_FSL python=3.8.13
conda activate MLL_FSL
pip install -r requirements.txt
python -m ipykernel install --user --name MLL_FSL
python setup.py develop
(orpython setup.py install
if you don't want to do development)
Download preprocessed features and trained models
- This data is over 20GB and we are currently in the process of finding a public drive to host the data.
cd ./scripts/
bash run_all_inductive.sh
to run all inductive resultsbash run_all_transductive.sh
to run all transductive results
For further questions or details, reach out to Samuel Hess ([email protected])
Special thanks to the authors of many prior works that have shared their code, including: