mlsanityData to 7 Oct 2026

The Effect of Diversity in Meta-Learning

arXiv 2201.11775 ↗First posted 27 Jan 2022cs.LGcited by 13

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Abstract (arXiv)

Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a significant improvement in performance as previously believed. To strengthen our findings, we provide both empirical and theoretical evidence.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

27 of 43 references.

References · 27

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  5. 2016Matching Networks for One Shot Learningcited by 2,246
  6. 2013Describing Textures in the Wildcited by 2,110
  7. 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
  8. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  9. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  10. 2019A Closer Look at Few-shot Classificationcited by 679
  11. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 577
  12. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
15 more
  1. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  2. 2018Conditional Neural Processescited by 421
  3. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  4. 2017Meta Networkscited by 268
  5. 2018Probabilistic Model-Agnostic Meta-Learningcited by 254
  6. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  7. 2019Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processescited by 131
  8. 2019Meta-Learning without Memorizationcited by 92
  9. 2018Unsupervised Learning via Meta-Learningcited by 81
  10. 2019Batch Active Learning Using Determinantal Point Processescited by 42
  11. 2020Adaptive Task Sampling for Meta-Learningcited by 29
  12. 2018Active Mini-Batch Sampling using Repulsive Point Processescited by 19
  13. 2020Task Augmentation by Rotating for Meta-Learningcited by 18
  14. 2020Is Support Set Diversity Necessary for Meta-Learning?cited by 14
  15. 2018Uncertainty in Multitask Transfer Learningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 2; 2025: 3; 2026: 2.

13 papers.

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  1. 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regressioncited by 138
  2. 2025Model Predictive Task Sampling for Efficient and Robust Adaptationcited by 20
  3. 2023Towards Task Sampler Learning for Meta-Learningcited by 11
  4. 2024On the Role of Depth and Looping for In-Context Learning with Task Diversitycited by 11
  5. 2022The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalencecited by 10
  6. 2023Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimizationcited by 6
  7. 2023Is Pre-training Truly Better Than Meta-Learning?cited by 6
  8. 2025DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Taskscited by 2
  9. 2023Exploring Active Learning in Meta-Learning: Enhancing Context Set Labelingcited by 1
  10. 2024Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distributioncited by 1
  11. 2025Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Modelscited by 0
  12. 2026Meta-learning to Address Data Shift in Time Series Classificationcited by 0
1 more
  1. 2026PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classificationcited by 0