AutoML: A survey of the state-of-the-art

Xin He, Kaiyong Zhao, Xiaowen CHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Deep learning (DL) techniques have obtained remarkable achievements on various tasks, such as image recognition, object detection, and language modeling. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering its wide application. Meanwhile, automated machine learning (AutoML) is a promising solution for building a DL system without human assistance and is being extensively studied. This paper presents a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. According to the DL pipeline, we introduce AutoML methods – covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS) – with a particular focus on NAS, as it is currently a hot sub-topic of AutoML. We summarize the representative NAS algorithms’ performance on the CIFAR-10 and ImageNet datasets and further discuss the following subjects of NAS methods: one/two-stage NAS, one-shot NAS, joint hyperparameter and architecture optimization, and resource-aware NAS. Finally, we discuss some open problems related to the existing AutoML methods for future research.

Original languageEnglish
Article number106622
JournalKnowledge-Based Systems
Volume212
DOIs
Publication statusPublished - 5 Jan 2021

Scopus Subject Areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

User-Defined Keywords

  • Automated machine learning (autoML)
  • Deep learning
  • Hyperparameter optimization (HPO)
  • Neural architecture search (NAS)

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