@Article{ZhengyingLiu2021,
author="Zhengying Liu
and Adrien Pavao
and Zhen Xu
and Sergio Escalera
and Fabio Ferreira
and Isabelle Guyon
and Sirui Hong
and Frank Hutter
and Rongrong Ji
and Julio C. S. Jacques Junior
and Ge Li
and Marius Lindauer
and Zhipeng Luo
and Meysam Madadi
and Thomas Nierhoff
and Kangning Niu
and Chunguang Pan
and Danny Stoll
and Sebastien Treguer
and Jin Wang
and Peng Wang
and Chenglin Wu
and Youcheng Xiong
and Arber Zela
and Yang Zhang",
title="Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019",
journal="IEEE Transactions on Pattern Analysis and Machine Intelligence",
year="2021",
volume="43",
number="9",
pages="3108--3125",
abstract="This paper reports the results and post-challenge analyses of ChaLearn{\textquoteright}s AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a {\textquoteleft}{\textquoteleft}meta-learner{\textquoteright}{\textquoteright}, {\textquoteleft}{\textquoteleft}data ingestor{\textquoteright}{\textquoteright}, {\textquoteleft}{\textquoteleft}model selector{\textquoteright}{\textquoteright}, {\textquoteleft}{\textquoteleft}model/learner{\textquoteright}{\textquoteright}, and {\textquoteleft}{\textquoteleft}evaluator{\textquoteright}{\textquoteright}. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free {\textquoteleft}{\textquoteleft}AutoDL self-service.{\textquoteright}{\textquoteright}",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3587), last updated on Tue, 23 Nov 2021 13:17:29 +0100",
doi="10.1109/TPAMI.2021.3075372",
opturl="https://ieeexplore.ieee.org/abstract/document/9415128"
}