@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;MILAB", 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" }