Competition on Few Shot Learning
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Jan N. van Rijn
Sep 18, 2021, 11:06:23 PM9/18/21
to Machine Learning News
Dear ML News,
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 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.
The competition has a focus on fast solutions. Per submission, the user will get 2 hours of calculations on each meta-dataset. Presumably, this competition favors first-order solutions (such as FO-MAML, LSTM meta-learner) over second-order solutions (such as MAML, TURTLE).
More information can be found on our website: https://metalearning.chalearn.org/
I hope that many of you will be able to participate!
Best,
Jan N. van Rijn
also on behalf of Isabelle Guyon and Joaquin Vanschoren
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 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.
The competition has a focus on fast solutions. Per submission, the user will get 2 hours of calculations on each meta-dataset. Presumably, this competition favors first-order solutions (such as FO-MAML, LSTM meta-learner) over second-order solutions (such as MAML, TURTLE).
More information can be found on our website: https://metalearning.chalearn.org/
I hope that many of you will be able to participate!
Best,
Jan N. van Rijn
also on behalf of Isabelle Guyon and Joaquin Vanschoren
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