The Synergistic Role of Deep Learning and Neural Architecture Search in Advancing Artificial Intelligence

Abstract

This paper delves into the significance and interaction between deep learning (DL) and neural architecture search (NAS) within the realm of artificial intelligence. As DL has become integral in addressing complex problems through its robust learning capabilities, NAS offers the potential to autonomously discover efficient neural network structures. The fusion of these technologies not only enhances model performance but also accelerates the proliferation of AI applications, particularly in resource-constrained settings such as embedded devices and edge computing. This study provides an in-depth comparative analysis of multiple neural network models applied to the CIFAR-10 dataset, with a particular focus on the performance of the Darts-SEBnet model. By incorporating a self-attention mechanism, the Darts-SEBnet model demonstrates a significant improvement in accuracy over the baseline VGG16 model. Furthermore, the paper reviews the evolution of NAS, emphasizing the success of gradient-based search methods like DARTS and its variants in improving search efficiency and model performance. The findings suggest that the integration of DL and NAS could drive further innovations in AI, offering solutions to existing bottlenecks and expanding AI's applicability across diverse fields.