  Deep learning (DL) techniques have penetrated all aspects of our lives and
brought us great convenience. However, building a high-quality DL system for a
specific task highly relies on human expertise, hindering the applications of
DL to more areas. Automated machine learning (AutoML) becomes a promising
solution to build a DL system without human assistance, and a growing number of
researchers focus on AutoML. In this paper, we provide a comprehensive and
up-to-date review of the state-of-the-art (SOTA) in AutoML. First, we introduce
AutoML methods according to the pipeline, covering data preparation, feature
engineering, hyperparameter optimization, and neural architecture search (NAS).
We focus more on NAS, as it is currently very hot sub-topic of AutoML. We
summarize the performance of the representative NAS algorithms on the CIFAR-10
and ImageNet datasets and further discuss several worthy studying directions of
NAS methods: one/two-stage NAS, one-shot NAS, and joint hyperparameter and
architecture optimization. Finally, we discuss some open problems of the
existing AutoML methods for future research.
