Domain-Adaptive Neural Architecture Search: A Unified Framework for Vision, Language, Healthcare, and Edge Intelligence

Abstract

Neural Architecture Search (NAS) has emerged recently as a powerful paradigm for automating deep neural network design. However, most existing NAS methods are optimised for a single domain, limiting their generalisation to diverse application areas such as computer vision, natural language processing, healthcare, speech recognition, and edge intelligence. This paper proposes a Domain-Adaptive Neural Architecture Search (DA-NAS) framework that learns domain-aware architectural patterns while it maintains a shared search space and optimisation strategy. DA-NAS combines domain embeddings, multi-objective optimisation, and resource-awareness to generate architectures that adapt to heterogeneous data characteristics and deployment constraints. Extensive experiments across multiple domains demonstrate that the proposed approach reduces search cost and improves cross-domain transferability, consistently outperforming domain-specific handcrafted models and conventional NAS baselines.