Super Network

Super-networks are large, overparameterized neural networks designed to encompass a vast number of smaller, specialized sub-networks. Research focuses on efficiently generating and training these super-networks, often employing techniques like Once-For-All training and Neural Architecture Transfer to extract high-performing sub-networks tailored to specific tasks or resource constraints. This approach aims to improve the efficiency and effectiveness of neural architecture search, leading to better performing models with reduced computational costs across diverse applications, from video processing to writer identification. The development of automated super-network generation tools further enhances accessibility and reproducibility within the field.