@InProceedings{ZhengyingLiu2019,
author="Zhengying Liu
and Isabelle Guyon
and Julio C. S. Jacques Junior
and Meysam Madadi
and Sergio Escalera
and Adrien Pavao
and Hugo Jair Escalante
and Wei-Wei Tu
and Zhen Xu
and Sebastien Treguer",
title="AutoCV Challenge Design and Baseline Results",
booktitle="La Conference sur l{\textquoteright}Apprentissage Automatique",
year="2019",
abstract="We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants{\textquoteright}s code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3323), last updated on Mon, 06 Mar 2023 15:46:39 +0100",
opturl="https://hal.archives-ouvertes.fr/hal-02265053/",
file=":http://refbase.cvc.uab.es/files/LGJ2019.pdf:PDF"
}