MetaLearn Challenges

MetaDL @ NeurIPS'21

Following the success of the AutoDL 2019-2020 challenge series, including an official competition of NeurIPS'19, and the first Meta-Learning challenge at AAAI'21, we are organizing MetaDL, a few-shot-learning competition challenging DL methods for meta-learning at NeurIPS'21.

We are grateful to Microsoft and Google for generous cloud unit donations. This project is also supported by ChaLearn and HUMANIA chair of AI grant ANR-19-CHIA-00222 of the Agence Nationale de la Recherche (France). Researchers and students from Université Paris-Saclay, Universiteit Leiden, TU Eindhoven, and 4Paradigm have contributed. The challenge is hosted by Codalab (Université Paris-Saclay).

Meta-learning competition at NeurIPS 2021

Provisional schedule:

    • Aug 2, 2021: Track 1 opens, few-shot-learning from 128x128 color images from 5 different domains.

    • Aug 20, 2021: Challenge bootcamp where newcomers to the field can learn how to get started, with expert coaching.

    • Sep 2, 2021: Track 2 opens, few-shot-learning from tabular data, from 5 different domains.

    • Oct 2, 2021: End of both challenge tracks.

    • Dec 6-14: NeurIPS conference.

New Benchmark for Meta-Learning

We have worked hard to come up with several new meta-learning 128x128 image datasets. We feel that the meta-learning community can greatly benefit from a new benchmark, and plan to publish these datasets open source after the challenge. It is our goal to establish this as a new benchmark for the meta-learning community. Participating in this challenge will give you a head start at getting to work with these datasets.

The challenge is with code submission and will be run on the Codalab platform with generous donations of cloud units from Microsoft and Google. The challenge winners will receive prizes donated by ChaLearn, if they agree to open-source their code. However, there is no such requirement to enter the challenge. The top ranking participants will be invited to co-author a paper on the challenge results, planned to be published in the PMLR, the proceedings track of the Journal of Machine Learning Research.

Congratulations to the AAAI 2021 MetaDL winners

About Meta Learning

For a comprehensive overview of Meta-learning, we refer to the following resources:

ChaLearn (USA)

University Paris-Saclay (France)

Codalab, UPSaclay (France)

Leiden University (the Netherlands)

TU Eindhoven (the Netherlands)

4Paradigm (China)