Torchmeta: A Meta-Learning library for PyTorch
Tristan Deleu, Tobias W\"urfl, Mandana Samiei, Joseph Paul Cohen,, Yoshua Bengio

TL;DR
Torchmeta is a PyTorch library that simplifies the development and evaluation of meta-learning algorithms across multiple standardized benchmarks, promoting fair comparison and easier dataset handling.
Contribution
It introduces a unified data-loading framework and extensions for PyTorch to facilitate meta-learning research on various datasets.
Findings
Provides data-loaders for standard meta-learning benchmarks
Enables seamless evaluation across multiple datasets
Simplifies model development for meta-learning algorithms
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Machine Learning and Data Classification · Multimodal Machine Learning Applications
