Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Exploring the Intersection between Neural Architecture Search and Continual Learning

Mohamed Shahawy, Elhadj Benkhelifa, David White

TL;DR

The paper addresses the need for autonomous, continually adaptive neural systems by formalizing the convergence of Neural Architecture Search (NAS) and Continual Learning (CL) into the Continually-Adaptive Neural Networks (CANNs) paradigm. It provides a structured review of CL and NAS, introducing a taxonomy of memory-based CL approaches, NAS search spaces, algorithms, and evaluation strategies, and then articulates a cohesive CANN framework with data automation and model plasticity. Key contributions include a formal definition of CANNs, a comprehensive synthesis of existing CL/NAS work, and a roadmap of desiderata and future directions to enable lifelong autonomous learning with bounded resources. The work highlights practical implications for edge devices and autonomous systems by outlining how end-to-end automation, domain-agnostic adaptation, and graceful forgetting can be achieved, potentially advancing toward more general AI capabilities. Overall, the paper sets a foundation for integrating continual adaptation into automated architecture design, identifying gaps and proposing actionable avenues for research and development.

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.

Exploring the Intersection between Neural Architecture Search and Continual Learning

TL;DR

The paper addresses the need for autonomous, continually adaptive neural systems by formalizing the convergence of Neural Architecture Search (NAS) and Continual Learning (CL) into the Continually-Adaptive Neural Networks (CANNs) paradigm. It provides a structured review of CL and NAS, introducing a taxonomy of memory-based CL approaches, NAS search spaces, algorithms, and evaluation strategies, and then articulates a cohesive CANN framework with data automation and model plasticity. Key contributions include a formal definition of CANNs, a comprehensive synthesis of existing CL/NAS work, and a roadmap of desiderata and future directions to enable lifelong autonomous learning with bounded resources. The work highlights practical implications for edge devices and autonomous systems by outlining how end-to-end automation, domain-agnostic adaptation, and graceful forgetting can be achieved, potentially advancing toward more general AI capabilities. Overall, the paper sets a foundation for integrating continual adaptation into automated architecture design, identifying gaps and proposing actionable avenues for research and development.

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.
Paper Structure (36 sections, 5 equations, 7 figures, 3 tables, 1 algorithm)

This paper contains 36 sections, 5 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Biological Inspiration
  3. Motivation, Scope, and Contributions to Knowledge
  4. Continual Learning Approaches
  5. Replay-Based Methods
  6. Parameter-Constraint Methods
  7. Evaluation of Continual Learning Performance
  8. Neural Architecture Search Approaches
  9. Search Spaces
  10. Layer-Wise Space
  11. Cell-Based Space
  12. Hierarchical Space
  13. Other Search Spaces
  14. Search Algorithms
  15. Random/Grid Search
  16. ...and 21 more sections

Figures (7)

  • Figure 1: Comparative illustrations for Adaptive Learning and its neighboring Machine Learning paradigms (inspired by delange_2021)
  • Figure 2: Continual Learning memory-retention strategies
  • Figure 3: The general Neural Architecture Search framework
  • Figure 4: Layer-Wise Search Space for Neural Architecture Search; A single-branch architecture sample (top) and a multi-branch counterpart (bottom)
  • Figure 5: Generic Cell-based Neural Architecture Search topology sample
  • ...and 2 more figures