mlsanityData to 7 Oct 2026

Torchmeta: A Meta-Learning library for PyTorch

arXiv 1909.06576 ↗First posted 14 Sep 2019cs.LG stat.MLcited by 52

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Abstract (arXiv)

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data-loaders for most of the standard benchmarks in few-shot classification and regression, with a new meta-dataset abstraction. It also features some extensions for PyTorch to simplify the development of models compatible with meta-learning algorithms. The code is available here: https://github.com/tristandeleu/pytorch-meta

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2019.

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Citing papers per year: 2020: 1; 2021: 11; 2022: 15; 2023: 13; 2024: 6; 2025: 5; 2026: 1.

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  1. 2020Offline Meta-Reinforcement Learning with Advantage Weightingcited by 92
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  4. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  5. 2022Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learningcited by 57
  6. 2022A Modern Self-Referential Weight Matrix That Learns to Modify Itselfcited by 40
  7. 2021Learning where to learn: Gradient sparsity in meta and continual learningcited by 33
  8. 2022Betty: An Automatic Differentiation Library for Multilevel Optimizationcited by 27
  9. 2022TorchOpt: An Efficient Library for Differentiable Optimizationcited by 17
  10. 2023VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distributioncited by 17
  11. 2023Making Scalable Meta Learning Practicalcited by 17
  12. 2021A contrastive rule for meta-learningcited by 14
38 more
  1. 2021A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learningcited by 12
  2. 2023Meta-Learning with a Geometry-Adaptive Preconditionercited by 11
  3. 2022The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalencecited by 10
  4. 2023Meta-learning in healthcare: A surveycited by 9
  5. 2024Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformerscited by 9
  6. 2021Minimax Optimization with Smooth Algorithmic Adversariescited by 8
  7. 2021Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awarenesscited by 8
  8. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  9. 2022p-Meta: Towards On-device Deep Model Adaptationcited by 8
  10. 2023Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopescited by 8
  11. 2023Is Pre-training Truly Better Than Meta-Learning?cited by 6
  12. 2023Metalearning Continual Learning Algorithmscited by 6
  13. 2022Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approachcited by 5
  14. 2022Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networkscited by 4
  15. 2022Frame Interpolation for Dynamic Scenes with Implicit Flow Encodingcited by 4
  16. 2022Adaptive Prototypical Networkscited by 4
  17. 2023Federated Conditional Stochastic Optimizationcited by 4
  18. 2025Differentiable Model Predictive Control on the GPUcited by 4
  19. 2024XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coveragecited by 3
  20. 2021Rapid Model Architecture Adaption for Meta-Learningcited by 2
  21. 2021Does MAML Only Work via Feature Re-use? A Data Centric Perspectivecited by 2
  22. 2022MAC: A Meta-Learning Approach for Feature Learning and Recombinationcited by 1
  23. 2023SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learningcited by 1
  24. 2023Understanding Transfer Learning and Gradient-Based Meta-Learning Techniquescited by 1
  25. 2024Any-Way Meta Learningcited by 1
  26. 2024Fully First-Order Methods for Decentralized Bilevel Optimizationcited by 1
  27. 2025Learning to segment anatomy and lesions from disparately labeled sources in brain MRIcited by 1
  28. 2021Will Multi-modal Data Improves Few-shot Learning?cited by 0
  29. 2021The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalencecited by 0
  30. 2022It's DONE: Direct ONE-shot learning with quantile weight imprintingcited by 0
  31. 2022Multi-Modal Fusion by Meta-Initializationcited by 0
  32. 2023Learning to Learn with Indispensable Connectionscited by 0
  33. 2023Accelerating Meta-Learning by Sharing Gradientscited by 0
  34. 2023Exploring intra-task relations to improve meta-learning algorithmscited by 0
  35. 2024On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalizationcited by 0
  36. 2024Fast Adaptation with Kernel and Gradient based Meta Leaningcited by 0
  37. 2025Riemannian Geometric-based Meta Learningcited by 0
  38. 2025Domain-Generalization to Improve Learning in Meta-Learning Algorithmscited by 0

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