Training Free Metric

Training-free metrics aim to evaluate the performance of neural network architectures without the computationally expensive process of training, significantly accelerating neural architecture search (NAS). Current research focuses on developing more robust and accurate training-free metrics, often combining multiple metrics or incorporating techniques like Bayesian optimization and evolutionary algorithms to improve search efficiency and accuracy. These advancements are crucial for making NAS more accessible and practical, enabling the design of high-performing models for various applications with reduced computational resources. The development of more effective training-free metrics is a key step towards more efficient and scalable automated machine learning.