mlsanityData to 7 Oct 2026

Data Augmentation for Meta-Learning

arXiv 2010.07092 ↗First posted 14 Oct 2020cs.LG cs.CVcited by 38

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Abstract (arXiv)

Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms sample support data, query data, and tasks on each training step. In this complex sampling scenario, data augmentation can be used not only to expand the number of images available per class, but also to generate entirely new classes/tasks. We systematically dissect the meta-learning pipeline and investigate the distinct ways in which data augmentation can be integrated at both the image and class levels. Our proposed meta-specific data augmentation significantly improves the performance of meta-learners on few-shot classification benchmarks.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 12; 2023: 11; 2024: 2; 2025: 4; 2026: 1.

38 papers.

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  1. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  2. 2022C-Mixup: Improving Generalization in Regressioncited by 57
  3. 2021Meta-Learning with Fewer Tasks through Task Interpolationcited by 45
  4. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  5. 2021Few-Shot Learning with Class Imbalancecited by 18
  6. 2022What Matters For Meta-Learning Vision Regression Tasks?cited by 18
  7. 2023Run-Off Election: Improved Provable Defense against Data Poisoning Attackscited by 16
  8. 2021On sensitivity of meta-learning to support datacited by 15
  9. 2022Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregationcited by 14
  10. 2024Robust Fast Adaptation from Adversarially Explicit Task Distribution Generationcited by 12
  11. 2022Set-based Meta-Interpolation for Few-Task Meta-Learningcited by 7
  12. 2023MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classificationcited by 7
26 more
  1. 2022How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentationcited by 6
  2. 2022Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptilecited by 6
  3. 2023Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imaginationcited by 6
  4. 2021Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learningcited by 5
  5. 2025Dual-level Mixup for Graph Few-shot Learning with Fewer Taskscited by 5
  6. 2023DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learningcited by 4
  7. 2023MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Taskscited by 4
  8. 2023Task-Distributionally Robust Data-Free Meta-Learningcited by 4
  9. 2021Semi-Supervised Few-Shot Intent Classification and Slot Fillingcited by 3
  10. 2021Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learningcited by 3
  11. 2022Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approachcited by 3
  12. 2022A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profilingcited by 2
  13. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  14. 2022Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognitioncited by 1
  15. 2022Interval Bound Interpolation for Few-shot Learning with Few Taskscited by 1
  16. 2023Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learningcited by 1
  17. 2023Exploring Active Learning in Meta-Learning: Enhancing Context Set Labelingcited by 1
  18. 2023RAFIC: Retrieval-Augmented Few-shot Image Classificationcited by 1
  19. 2021Meta-learning Amidst Heterogeneity and Ambiguitycited by 0
  20. 2021On Hard Episodes in Meta-Learningcited by 0
  21. 2022Learning to generate imaginary tasks for improving generalization in meta-learningcited by 0
  22. 2023Improving Generalization in Meta-Learning via Meta-Gradient Augmentationcited by 0
  23. 2024Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classificationcited by 0
  24. 2025TLXML: Task-Level Explanation of Meta-Learning via Influence Functionscited by 0
  25. 2025Task-Level Contrastiveness for Cross-Domain Few-Shot Learningcited by 0
  26. 2026Can One-Shot Test-Time Data Augmentation Help with Generalization?cited by 0