%0 Journal Article %1 triantafillou2019metadataset %A Triantafillou, Eleni %A Zhu, Tyler %A Dumoulin, Vincent %A Lamblin, Pascal %A Evci, Utku %A Xu, Kelvin %A Goroshin, Ross %A Gelada, Carles %A Swersky, Kevin %A Manzagol, Pierre-Antoine %A Larochelle, Hugo %D 2019 %K datasets few-shot %T Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples %U http://arxiv.org/abs/1903.03096 %X 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.

@article{triantafillou2019metadataset, abstract = {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.}, added-at = {2020-03-11T20:33:26.000+0100}, author = {Triantafillou, Eleni and Zhu, Tyler and Dumoulin, Vincent and Lamblin, Pascal and Evci, Utku and Xu, Kelvin and Goroshin, Ross and Gelada, Carles and Swersky, Kevin and Manzagol, Pierre-Antoine and Larochelle, Hugo}, biburl = {https://www.bibsonomy.org/bibtex/29ab0f9370b0a68ebada4fb6080c1d350/kirk86}, description = {[1903.03096] Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples}, interhash = {a4eb23e5268ef6fbfd4c5a1329acceb0}, intrahash = {9ab0f9370b0a68ebada4fb6080c1d350}, keywords = {datasets few-shot}, note = {cite arxiv:1903.03096Comment: Code available at https://github.com/google-research/meta-dataset}, timestamp = {2020-03-11T20:33:26.000+0100}, title = {Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples}, url = {http://arxiv.org/abs/1903.03096}, year = 2019 }