PT Unknown
AU Dustin Carrion Ojeda
   Hong Chen
   Adrian El Baz
   Sergio Escalera
   Chaoyu Guan
   Isabelle Guyon
   Ihsan Ullah
   Xin Wang
   Wenwu Zhu
TI NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results
BT Understanding Social Behavior in Dyadic and Small Group Interactions
PY 2022
BP 24
EP 37
VL 191
AB 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.
ER