Exploring the Intersection between Neural Architecture Search and Continual Learning
This work addresses the need for robust and adaptive agents in domains like IoT devices and self-driving vehicles, where model accessibility is limited after deployment, but it is incremental as it primarily reviews and formalizes existing concepts.
This paper tackles the problem of designing adaptive neural networks that can evolve post-deployment by conducting the first extensive review on the intersection of Neural Architecture Search (NAS) and Continual Learning (CL), formalizing the Continually-Adaptive Neural Networks (CANNs) paradigm and outlining research directions for lifelong autonomous ANNs.
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.