Free Neural Architecture Search

Free neural architecture search (NAS) aims to automate the design of efficient neural networks without the computationally expensive process of training numerous candidate architectures. Current research focuses on developing effective "zero-cost" proxy metrics that correlate with model performance, enabling efficient architecture ranking across various model types, including transformers, recurrent neural networks, and convolutional neural networks for image and video processing. This training-free approach significantly reduces the computational burden of NAS, making it more accessible and applicable to larger and more complex models, ultimately accelerating the development of more efficient and powerful AI systems.