TY  - CONF
AU  - Dustin Carrion Ojeda
AU  - Hong Chen
AU  - Adrian El Baz
AU  - Sergio Escalera
AU  - Chaoyu Guan
AU  - Isabelle Guyon
AU  - Ihsan Ullah
AU  - Xin Wang
AU  - Wenwu Zhu
A2  - PMLR
PY  - 2022//
TI  - NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results
BT  - Understanding Social Behavior in Dyadic and Small Group Interactions
SP  - 24
EP  - 37
VL  - 191
N2  - 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.
UR  - https://proceedings.mlr.press/v191/carrion-ojeda22a
L1  - http://refbase.cvc.uab.es/files/CCB2022.pdf
N1  - HUPBA; no menciona
ID  - Dustin Carrion Ojeda2022
ER  -