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Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

Wednesday, May 13, 2020

Posted by Eleni Triantafillou, Student Researcher and Vincent Dumoulin, Research Scientist, Google Researchmanually annotated training datasparked interestMeta-Dataset: A Dataset of Datasets for Learning to Learn from Few ExamplesICLR 2020ImageNetCUB-200-2011FungipublicnotebookTensorFlowPyTorchBackground: Few-shot Classificationtest taskstest setsupport set query set Comparison of Meta-Dataset with Previous Benchmarksmini-ImageNetImageNetclass splitsRecentworks

Test tasks from mini-ImageNet. Each task is a classification problem between previously unseen (test) classes. The model can use the support set of a few labeled examples of the new classes to adapt to the task at hand and then predicts labels for the query examples of these new classes. The evaluation metric is the query set accuracy, averaged over examples within each task and across tasks.

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Test tasks from Meta-Dataset. Contrary to the mini-ImageNet tasks shown above, different tasks here originate from (the test classes of) different datasets. Further, the number of classes and the support set sizes differ across tasks and the support sets might be class-imbalanced.

Initial Investigation and Findings on Meta-Datasetpre-trainingmeta-learningnearest-neighbor comparisonssurgeofattention1) Existing approaches have trouble leveraging heterogeneous training data sources.OmniglotQuickdraw

Comparison of test performance on each dataset after having trained on ImageNet (ILSVRC-2012) only or on all datasets.

2) Some models are more capable than others of exploiting additional data at test time.ProtoNetfo-Proto-MAML

Comparison of test performance averaged across different datasets to the number of examples available per class in test tasks (“shots”). Performance is measured in terms of class precision: the proportion of the examples of a class that are correctly labeled, averaged across classes.

3) The adaptation algorithm of a meta-learner is more heavily responsible for its performance than the fact that it is trained end-to-end (i.e. meta-trained).

Comparison of three different meta-learner variants to their corresponding inference-only baselines, when training on ImageNet (ILSVRC-1012) only or all datasets. Each bar represents the difference between meta-training and inference-only, so positive values indicate improved performance from meta-training.

Conclusiontaskconditioninghyperparameter tuningmeta-baselinefeature selectionAcknowledgementsMeta-Dataset was developed by Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol and Hugo Larochelle. We would like to thank Pablo Castro for his valuable guidance on this blog post, Chelsea Finn for fruitful discussions and ensuring the correctness of fo-MAML’s implementation, as well as Zack Nado and Dan Moldovan for the initial dataset code that was adapted, Cristina Vasconcelos for spotting an issue in the ranking of models and John Bronskill for suggesting that we experiment with a larger inner-loop learning rate for MAML which indeed significantly improved our fo-MAML results.