Supernet Optimization

Supernet optimization is a crucial technique in neural architecture search (NAS), aiming to efficiently find high-performing neural network architectures by training a single, large "supernet" encompassing many potential architectures. Current research focuses on improving the supernet's training process to enhance the accuracy and transferability of discovered architectures, addressing issues like performance collapse and order-preserving ability through methods such as refined search strategies and adaptive pruning. These advancements are significant because they improve the efficiency and effectiveness of NAS, leading to better-performing models for various applications, including image classification and spatiotemporal data analysis.