learn2learn: A Library for Meta-Learning Research
Abstract
Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility. Researchers are prone to make mistakes when prototyping new algorithms and tasks because modern meta-learning methods rely on unconventional functionalities of machine learning frameworks. In turn, reproducing existing results becomes a tedious endeavour -- a situation exacerbated by the lack of standardized implementations and benchmarks. As a result, researchers spend inordinate amounts of time on implementing software rather than understanding and developing new ideas. This manuscript introduces learn2learn, a library for meta-learning research focused on solving those prototyping and reproducibility issues. learn2learn provides low-level routines common across a wide-range of meta-learning techniques (e.g. meta-descent, meta-reinforcement learning, few-shot learning), and builds standardized interfaces to algorithms and benchmarks on top of them. In releasing learn2learn under a free and open source license, we hope to foster a community around standardized software for meta-learning research.
Cite
@article{arxiv.2008.12284,
title = {learn2learn: A Library for Meta-Learning Research},
author = {Sébastien M. R. Arnold and Praateek Mahajan and Debajyoti Datta and Ian Bunner and Konstantinos Saitas Zarkias},
journal= {arXiv preprint arXiv:2008.12284},
year = {2020}
}
Comments
Software available at: https://github.com/learnables/learn2learn