The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data-loaders for most of the standard benchmarks in few-shot classification and regression, with a new meta-dataset abstraction. It also features some extensions for PyTorch to simplify the development of models compatible with meta-learning algorithms. The code is available here: https://github.com/tristandeleu/pytorch-meta
@article{arxiv.1909.06576,
title = {Torchmeta: A Meta-Learning library for PyTorch},
author = {Tristan Deleu and Tobias Würfl and Mandana Samiei and Joseph Paul Cohen and Yoshua Bengio},
journal= {arXiv preprint arXiv:1909.06576},
year = {2019}
}