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.

翻译：深入学习(DL)技术深入了我们生活的各个方面,为我们带来了极大的便利。然而,为了一项高度依赖人类专门知识的具体任务而建立高质量的DL系统,妨碍了DL在更多领域的应用。自动机学习(Automal)成为在没有人力援助的情况下建立DL系统的一个很有希望的解决办法,越来越多的研究人员关注AutomalML。在本文中,我们提供了对Automal(SOTA)中最先进的方法的全面和最新审查。首先,我们根据管道采用自动ML方法,包括数据准备、特征工程、超光谱优化和神经结构搜索(NAS)。我们更多地关注NAS,因为它目前是AutomoML非常热的子题。我们总结了有代表性的NAS在CIFAR-10和图像网络数据集方面的算法的绩效,并进一步讨论了NAS方法的若干值得研究的方向:一/两阶段NAS,一发式NAS,一发式NAS,以及联合超分辨率和结构优化。最后,我们讨论了现有的自动ML方法中的一些公开的问题,供未来研究。