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.

深度学习（DL）技术在各种任务（例如图像识别，对象检测和语言建模）上均取得了非凡的成就。然而，针对特定任务构建高质量的DL系统高度依赖于人类的专业知识，从而阻碍了其广泛应用。同时，自动化机器学习（AutoML）是一种无需人为帮助即可构建DL系统的有前途的解决方案，并且正在广泛研究中。本文对AutoML中的最新技术（SOTA）进行了全面而最新的回顾。根据DL管道，我们介绍了AutoML方法-涉及数据准备，特征工程，超参数优化和神经体系结构搜索（NAS）-尤其着重于NAS，因为它是AutoML的热门子主题。我们总结了具有代表性的NAS算法在CIFAR-10和ImageNet数据集上的性能，并进一步讨论了NAS方法的以下主题：一阶段/两阶段NAS，单次NAS，联合超参数和体系结构优化以及资源感知型NAS。最后，我们讨论了一些与现有AutoML方法相关的未解决问题，以供将来研究。