Despite the significant advances achieved in Artificial Neural Networks
(ANNs), their design process remains notoriously tedious, depending primarily
on intuition, experience and trial-and-error. This human-dependent process is
often time-consuming and prone to errors. Furthermore, the models are generally
bound to their training contexts, with no considerations of changes to their
surrounding environments. Continual adaptability and automation of neural
networks is of paramount importance to several domains where model
accessibility is limited after deployment (e.g IoT devices, self-driving
vehicles, etc). Additionally, even accessible models require frequent
maintenance post-deployment to overcome issues such as Concept/Data Drift,
which can be cumbersome and restrictive. The current state of the art on
adaptive ANNs is still a premature area of research; nevertheless, Neural
Architecture Search (NAS), a form of AutoML, and Continual Learning (CL) have
recently gained an increasing momentum in the Deep Learning research field,
aiming to provide more robust and adaptive ANN development frameworks. This
study is the first extensive review on the intersection between AutoML and CL,
outlining research directions for the different methods that can facilitate
full automation and lifelong plasticity in ANNs.

这篇论文是第一篇广泛的综述文章，阐述了人工神经网络、持续学习和自动机器学习之间的交叉领域，并提出了各种方法以促进完全自动化和神经网络的终身可塑性。