@InProceedings{DustinCarrionOjeda2022,
author="Dustin Carrion Ojeda
and Hong Chen
and Adrian El Baz
and Sergio Escalera
and Chaoyu Guan
and Isabelle Guyon
and Ihsan Ullah
and Xin Wang
and Wenwu Zhu",
title="NeurIPS{\textquoteright}22 Cross-Domain MetaDL competition: Design and baseline results",
booktitle="Understanding Social Behavior in Dyadic and Small Group Interactions",
year="2022",
volume="191",
pages="24--37",
abstract="We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS{\textquoteright}22, focusing on {\textquoteleft}{\textquoteleft}cross-domain{\textquoteright}{\textquoteright} 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 {\textquoteleft}{\textquoteleft}any-way{\textquoteright}{\textquoteright} and {\textquoteleft}{\textquoteleft}any-shot{\textquoteright}{\textquoteright} 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 {\textquoteleft}{\textquoteleft}ways{\textquoteright}{\textquoteright} (within the range 2-20) and any number of {\textquoteleft}{\textquoteleft}shots{\textquoteright}{\textquoteright} (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.",
optnote="HUPBA; no menciona",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3802), last updated on Mon, 24 Apr 2023 15:32:28 +0200",
opturl="https://proceedings.mlr.press/v191/carrion-ojeda22a",
file=":http://158.109.8.37/files/CCB2022.pdf:PDF"
}