Dear Colleagues,

In artificial intelligence (AI), deep neural networks (DNNs) have revolutionized computer vision and achieved state-of-the-art results on various image tasks. However, designing an efficient DNN architecture is a challenging task that requires expert knowledge and extensive trial-and-error experiments.

Neural Architecture Search (NAS) is a cutting-edge approach revolutionizing the field of deep learning by automating the design of neural network architectures addressing critical challenges in manually designed deep neural networks. NAS, however, redefines this process by employing computational algorithms and search strategies to explore and identify optimal neural structures automatically. Its significance lies in its capacity to democratize the development of deep learning models, improve performance, and reduce the need for manual design. NAS encompasses a variety of search methods, including reinforcement learning, evolutionary algorithms, and gradient-based optimization, which traverse the vast design space of neural architectures. Through these strategies, NAS has not only proven its ability to discover architectures that surpass human-designed networks but also exhibited versatility across applications such as image classification, image segmentation, object detection, natural language processing, and so on.

NAS is constantly getting better. Researchers are working hard to make it faster, improve learning from different tasks and explore more possibilities in various application domains. This progress can transform AI systems into more accessible, efficient, and innovative ones. Research articles and reviews in this area of study are welcome.

We look forward to receiving your contributions.

Dr. Arjun Ghosh

Section Editor