# Advances in MetaDL: AAAI 2021 challenge and workshop

arXiv: https://arxiv.org/abs/2202.01890 · HTML: https://mlsanity.com/paper/2202.01890/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [A Survey of Deep Meta-Learning](https://mlsanity.com/paper/2010.03522/) (2020)
- [Leveraging the Feature Distribution in Transfer-based Few-Shot Learning](https://mlsanity.com/paper/2006.03806/) (2020)
- [Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019](https://mlsanity.com/paper/2201.03801/) (2022)
- [Few-Shot Learning for Road Object Detection](https://mlsanity.com/paper/2101.12543/) (2021)
- [The Algorithm Selection Competitions 2015 and 2017](https://mlsanity.com/paper/1805.01214/) (2018)
- [MetaDelta: A Meta-Learning System for Few-shot Image Classification](https://mlsanity.com/paper/2102.10744/) (2021)
- [Stateless Neural Meta-Learning using Second-Order Gradients](https://mlsanity.com/paper/2104.10527/) (2021)
- [Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network](https://mlsanity.com/paper/2102.05176/) (2021)
- [Learning Abstract Task Representations](https://mlsanity.com/paper/2101.07852/) (2021)
- [Learning to Continually Learn Rapidly from Few and Noisy Data](https://mlsanity.com/paper/2103.04066/) (2021)
- [Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning](https://mlsanity.com/paper/2009.11253/) (2020)
- [Stress Testing of Meta-learning Approaches for Few-shot Learning](https://mlsanity.com/paper/2101.08587/) (2021)

## Built on by (most cited first, 4 of 4)

- [Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification](https://mlsanity.com/paper/2206.08138/) (2022)
- [NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results](https://mlsanity.com/paper/2208.14686/) (2022)
- [Contrastive Meta-Learning for Partially Observable Few-Shot Learning](https://mlsanity.com/paper/2301.13136/) (2023)
- [Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round](https://mlsanity.com/paper/2208.02821/) (2022)

## Abstract (arXiv)

To stimulate advances in metalearning using deep learning techniques (MetaDL), we organized in 2021 a challenge and an associated workshop. This paper presents the design of the challenge and its results, and summarizes presentations made at the workshop. The challenge focused on few-shot learning classification tasks of small images. Participants' code submissions were run in a uniform manner, under tight computational constraints. This put pressure on solution designs to use existing architecture backbones and/or pre-trained networks. Winning methods featured various classifiers trained on top of the second last layer of popular CNN backbones, fined-tuned on the meta-training data (not necessarily in an episodic manner), then trained on the labeled support and tested on the unlabeled query sets of the meta-test data.