Exploring the Intersection between Neural Architecture Search and Continual Learning

TL;DR

This paper reviews the intersection of Neural Architecture Search and Continual Learning, proposing a new paradigm for lifelong adaptive neural networks to improve robustness and automation in dynamic environments.

Contribution

It formalizes the concept of Continually-Adaptive Neural Networks (CANNs) and outlines future research directions for integrating NAS and CL.

Findings

First comprehensive review of NAS and CL intersection

Formalization of the CANNs paradigm

Identification of key research challenges and directions

Abstract

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.