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

2022-08-31 08:31:02
Dustin Carrión-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

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.

Abstract (translated)

URL

https://arxiv.org/abs/2208.14686

PDF

https://arxiv.org/pdf/2208.14686.pdf


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