Deep learning (DL) has proven to be a highly effective approach for
developing models in diverse contexts, including visual perception, speech
recognition, and machine translation. However, the end-to-end process for
applying DL is not trivial. It requires grappling with problem formulation and
context understanding, data engineering, model development, deployment,
continuous monitoring and maintenance, and so on. Moreover, each of these steps
typically relies heavily on humans, in terms of both knowledge and
interactions, which impedes the further advancement and democratization of DL.
Consequently, in response to these issues, a new field has emerged over the
last few years: automated deep learning (AutoDL). This endeavor seeks to
minimize the need for human involvement and is best known for its achievements
in neural architecture search (NAS), a topic that has been the focus of several
surveys. That stated, NAS is not the be-all and end-all of AutoDL. Accordingly,
this review adopts an overarching perspective, examining research efforts into
automation across the entirety of an archetypal DL workflow. In so doing, this
work also proposes a comprehensive set of ten criteria by which to assess
existing work in both individual publications and broader research areas. These
criteria are: novelty, solution quality, efficiency, stability,
interpretability, reproducibility, engineering quality, scalability,
generalizability, and eco-friendliness. Thus, ultimately, this review provides
an evaluative overview of AutoDL in the early 2020s, identifying where future
opportunities for progress may exist.

该综述回顾了自动深度学习方向的研究进展，提出了十个评估标准，包括新颖性、解决方案质量、效率、稳定性、可解释性、再现性、工程质量、可扩展性、泛化性和环保性。