Network Architecture Search

Network architecture search (NAS) automates the design of optimal neural network architectures, aiming to improve model performance, efficiency, and robustness. Current research focuses on developing efficient NAS algorithms, such as those employing zero-cost proxies or evolutionary strategies, and applying them to diverse applications including image classification, object detection, and scientific imaging. These advancements lead to more accurate and resource-efficient models across various domains, impacting both scientific discovery and practical applications by reducing computational costs and improving performance. The field is also exploring NAS for enhancing model robustness to real-world data corruptions and optimizing architectures for specific hardware platforms.