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

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arxiv 1903.03096 v4 pith:6UGQ4363 submitted 2019-03-07 cs.LG stat.ML

Eleni Triantafillou , Tyler Zhu , Vincent Dumoulin , Pascal Lamblin , Utku Evci , Kelvin Xu , Ross Goroshin , Carles Gelada
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Kevin Swersky Pierre-Antoine Manzagol Hugo Larochelle
This is my paper
classification cs.LGstat.ML
keywords meta-datasetdatasetsmodelsproposebaselinesdiverseexampleslearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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 it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models' ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.

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