This repository contains the code for the main meta pre-training experiments with the simple meta-learning algorithm for continual learning (SiM4C) from the ICCV paper A Simple Recipe to Meta-Learn Forward and Backward Transfer. The code to download and preprocess the Omniglot dataset is based on the prior meta pre-training implementation from Javed et al. 2019.
We provide a configuration file to easily install dependencies via conda:
conda env create -f conda_env.yml
conda activate metaL
We provide a simple script to replicate our main experiments with SiM4C:
To perform both meta pre-training and meta-testing, run:
./scripts/train_sim4c.sh
To re-run meta-testing with different configurations, check the location of the experiment folder (ending in the current date) and run:
python eval_all.py experiment_path=path/to/experiment/folder
To run and evaluate alternative baselines execute train.py and eval.py/eval_all.py overriding the appropriate arguments (see hydra for details).
To reference this work in future research, you can use the following:
@InProceedings{Cetin_2023_ICCV,
author = {Cetin, Edoardo and Carta, Antonio and Celiktutan, Oya},
title = {A Simple Recipe to Meta-Learn Forward and Backward Transfer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {18732-18742}
}