Feature Norm

Feature norm, the magnitude of a feature vector in machine learning, is increasingly central to improving model performance and understanding neural network behavior. Current research focuses on leveraging feature norms for diverse applications, including enhancing federated learning by mitigating data heterogeneity, improving knowledge distillation through better feature alignment, and enabling more robust out-of-distribution detection. These advancements have significant implications for various fields, from improving the accuracy and efficiency of AI models to facilitating better understanding of human conceptual knowledge and the development of more trustworthy AI systems.