Shot Na

One-shot Neural Architecture Search (NAS) aims to efficiently discover optimal neural network architectures by training a single "supernet" encompassing many potential sub-networks, thereby avoiding the computationally expensive process of training each architecture individually. Current research focuses on improving the accuracy and consistency of performance predictions from the supernet by addressing the issue of weight sharing among sub-networks, employing techniques like adaptive sampling strategies and incorporating prior knowledge to guide the search process. These advancements are significant because they promise to accelerate the development of efficient and high-performing neural networks for various applications, reducing the computational cost and time required for NAS.