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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 · ORCID
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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development

    cs.LG 2026-07 conditional novelty 5.0 of 10

    In a stylized model, a proactive flywheel that fixes whole groups of related scenarios needs Θ(K log K) update rounds versus Θ(M log M) for reactive patching.

  2. Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts

    cs.CV 2025-05 conditional novelty 5.0 of 10

    PromptMargin adapts CLIP to few-shot classification under distribution shift using selective augmentations and a multimodal margin regularizer, beating MaPLe on most of fifteen datasets.

  3. CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning

    cs.CV 2025-09 conditional novelty 4.0 of 10

    CCoMAML, a Cooperative MAML variant with a CNN co-learner, reports strong few-shot cattle identification from muzzle images, but its test-set-tuned hyperparameters and best-split reporting weaken the result.

  4. ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation

    cs.CV 2025-07 reject novelty 2.0 of 10

    ViT-ProtoNet, a Prototypical Network with a ViT-Small encoder, is reported to reach 95-97% 5-shot accuracy on three benchmarks and 81.88% on FC100, but the evaluation lacks critical baselines.

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