TY - JOUR AU - Zhengying Liu AU - Adrien Pavao AU - Zhen Xu AU - Sergio Escalera AU - Fabio Ferreira AU - Isabelle Guyon AU - Sirui Hong AU - Frank Hutter AU - Rongrong Ji AU - Julio C. S. Jacques Junior AU - Ge Li AU - Marius Lindauer AU - Zhipeng Luo AU - Meysam Madadi AU - Thomas Nierhoff AU - Kangning Niu AU - Chunguang Pan AU - Danny Stoll AU - Sebastien Treguer AU - Jin Wang AU - Peng Wang AU - Chenglin Wu AU - Youcheng Xiong AU - Arber Zela AU - Yang Zhang PY - 2021// TI - Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 T2 - TPAMI JO - IEEE Transactions on Pattern Analysis and Machine Intelligence SP - 3108 EP - 3125 VL - 43 IS - 9 N2 - This paper reports the results and post-challenge analyses of ChaLearn'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 “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. 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 “AutoDL self-service.” UR - https://ieeexplore.ieee.org/abstract/document/9415128 UR - http://dx.doi.org/10.1109/TPAMI.2021.3075372 N1 - HUPBA; no proj;MILAB ID - Zhengying Liu2021 ER -