论文标题
探索神经体系结构搜索与持续学习之间的交集
Exploring the Intersection between Neural Architecture Search and Continual Learning
论文作者
论文摘要
尽管在人工神经网络（ANN）中取得了重大进展，但其设计过程仍在臭名昭著，这主要取决于直觉，经验和反复试验。这个依赖人类的过程通常很耗时，并且容易出现错误。此外，这些模型通常与其训练环境绑定，而没有考虑其周围环境。神经网络的持续适应性和自动化对部署后模型可访问性的几个领域至关重要（例如，IoT设备，自动驾驶汽车等）。此外，即使是可访问的模型，也需要经常维护后部署，以克服诸如概念/数据漂移之类的问题，这些问题可能繁琐且限制性。通过利用和结合神经体系结构搜索（NAS）和持续学习（CL）的方法，可以开发出更健壮和适应性的剂。这项研究对NAS和CL之间的交集进行了首次广泛的综述，正式化了前瞻性持续适应性神经网络（CANNS）范式，并概述了终身自治ANN的研究方向。
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 to their surrounding environments. Continual adaptiveness 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. By leveraging and combining approaches from Neural Architecture Search (NAS) and Continual Learning (CL), more robust and adaptive agents can be developed. This study conducts the first extensive review on the intersection between NAS and CL, formalizing the prospective Continually-Adaptive Neural Networks (CANNs) paradigm and outlining research directions for lifelong autonomous ANNs.
