Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

by   Eleni Triantafillou, et al.

Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle this recently, we find the current procedure and datasets that are used to systematically assess progress in this setting lacking. To address this, we propose Meta-Dataset: a new benchmark for training and evaluating few-shot classifiers that is large-scale, consists of multiple datasets, and presents more natural and realistic tasks. The aim is to measure the ability of state-of-the-art models to leverage diverse sources of data to achieve higher generalization, and to evaluate that generalization ability in a more challenging setting. We additionally measure robustness of current methods to variations in the number of available examples and the number of classes. Finally our extensive empirical evaluation leads us to identify weaknesses in Prototypical Networks and MAML, two popular few-shot classification methods, and to propose a new method, Proto-MAML, which achieves improved performance on our benchmark.


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Code Repositories


NeurIPS 2021 - Few-shot learning competition

view repo


Learning to reinforcement learn for Neural Architecture Search

view repo


meta learning from the initializaion induced by word embedding

view repo


Full implementation and re-production of the meta-learning algorithm REPTILE

view repo

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