mlsanityData to 7 Oct 2026

NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results

arXiv 2208.14686 ↗First posted 31 Aug 2022cs.LG cs.AI cs.CVcited by 3

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Abstract (arXiv)

We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve "any-way" and "any-shot" problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of "ways" (within the range 2-20) and any number of "shots" (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 31 Aug 2022.

Builds on

17 of 28 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  4. 2016Matching Networks for One Shot Learningcited by 2,246
  5. 2014OpenML: networked science in machine learningcited by 523
  6. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  7. 2018Task-Agnostic Meta-Learning for Few-shot Learningcited by 151
  8. 2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 79
  9. 2020Self-training for Few-shot Transfer Across Extreme Task Differencescited by 75
  10. 2020A Universal Representation Transformer Layer for Few-Shot Image Classificationcited by 65
  11. 2021Learning a Universal Template for Few-shot Dataset Generalizationcited by 61
  12. 2021Universal Representation Learning from Multiple Domains for Few-shot Classificationcited by 56
5 more
  1. 2022Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019cited by 16
  2. 2020Selecting Relevant Features from a Multi-domain Representation for Few-shot Classificationcited by 11
  3. 2021MetaDelta: A Meta-Learning System for Few-shot Image Classificationcited by 10
  4. 2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classificationcited by 10
  5. 2022Advances in MetaDL: AAAI 2021 challenge and workshopcited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classificationcited by 29
  2. 2025MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?cited by 27
  3. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2