  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.
