Name
Abstract | ||
|---|---|---|
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.” |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPAMI.2021.3075372 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
AutoML,deep learning,meta-learning,neural architecture search,model selection,hyperparameter optimization | Journal | 43 |
Issue | ISSN | Citations |
9 | 0162-8828 | 0 |
PageRank | References | Authors |
0.34 | 8 | 25 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengying Liu | 1 | 0 | 0.34 |
| Adrien Pavao | 2 | 0 | 0.34 |
| Zongxue Xu | 3 | 6 | 4.35 |
| Sergio Escalera Guerrero | 4 | 77 | 12.81 |
| Fábio Ferreira | 5 | 2 | 1.38 |
| Isabelle Guyon | 6 | 11033 | 1544.34 |
| David S. Hong | 7 | 3 | 0.77 |
| Frank Hutter | 8 | 2610 | 127.14 |
| Rongrong Ji | 9 | 3616 | 189.98 |
| Julio C. S. Jacques Junior | 10 | 22 | 5.50 |
| Ge Li | 11 | 23 | 7.11 |
| Marius Thomas Lindauer | 12 | 141 | 14.87 |
| Zhipeng Luo | 13 | 2 | 1.43 |
| Meysam Madadi | 14 | 87 | 9.28 |
| Thomas Nierhoff | 15 | 3 | 0.76 |
| Kangning Niu | 16 | 0 | 0.34 |
| Chunguang Pan | 17 | 0 | 2.03 |
| Danny Stoll | 18 | 0 | 0.34 |
| Sébastien Treguer | 19 | 0 | 1.01 |
| Jin Wang | 20 | 109 | 26.32 |
| Peng Wang | 21 | 62 | 26.65 |
| Chenglin Wu | 22 | 0 | 1.01 |
| Youcheng Xiong | 23 | 0 | 0.34 |
| Arber Zela | 24 | 9 | 3.14 |
| Yang Zhang | 25 | 58 | 37.96 |