NEURAL ARCHITECTURE SEARCH (NAS): AUTOMATING THE FUTURE OF AI MODEL DESIGN1

Abstract

This article provides a comprehensive overview of Neural Architecture Search (NAS), a revolutionary technique in artificial intelligence that automates the design of neural network architectures. It explores the fundamental concepts, key techniques, and wide-ranging applications of NAS across various domains of machine learning and computer vision. The article discusses the three main components of NAS systems: search space, search strategy, and performance estimation strategy, and delves into prominent approaches such as reinforcement learning, evolutionary algorithms, and gradient-based methods. It highlights the significant impact of NAS on image classification, natural language processing, object detection, and mobile/edge computing, demonstrating its potential to outperform manually designed models while reducing development time and computational resources. The article also addresses current challenges in NAS, including computational cost, search space design, and transferability, and outlines future research directions such as efficient search algorithms, multi-objective optimization, and expanding NAS to diverse AI tasks. Through this exploration, the paper underscores NAS's potential to revolutionize AI model development, making it more accessible, efficient, and effective across a wide range of applications.