SEMI SuperYOLO Na

Neural Architecture Search (NAS) techniques aim to automate the design of optimal neural network architectures for specific tasks, improving efficiency and performance. Current research focuses on developing NAS methods that handle multiple objectives (e.g., accuracy and efficiency), adapt to diverse datasets and hardware constraints, and operate efficiently in online or few-shot settings, often employing evolutionary algorithms, Bayesian optimization, or supernet-based approaches. These advancements are significant because they reduce the reliance on manual design, leading to more efficient and effective deep learning models across various applications, including image processing, robotics, and time series forecasting. The resulting optimized architectures are crucial for deploying deep learning on resource-constrained devices and for handling complex real-world problems.