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Tobias Würfl

Alumni

Publications

Torchmeta: A Meta-Learning library for PyTorch
Tristan Deleu
Tobias Würfl
Mandana Samiei
Joseph Paul Cohen
Yoshua Bengio
The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research… (see more). 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: this https URL
2019-09-14
ArXiv (preprint)
arxiv.org
arxiv.org