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

• URL: http://arxiv.org/abs/2208.14686v1
• Date: Wed, 31 Aug 2022 08:31:02 GMT
• Title: NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results
• Authors: Dustin Carri\'on-Ojeda (LISN, TAU), Hong Chen (CST), Adrian El Baz, Sergio Escalera (CVC), Chaoyu Guan (CST), Isabelle Guyon (LISN, TAU), Ihsan Ullah (LISN, TAU), Xin Wang (CST), Wenwu Zhu (CST)
• Abstract summary: We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22. This competition challenges the participants to solve "any-way" and "any-shot" problems drawn from various domains.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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