Weight Normalization

Weight normalization is a technique used in training deep neural networks to improve optimization and generalization performance by rescaling network weights. Current research focuses on theoretical analysis of its convergence properties, particularly in relation to Hessian matrix characteristics and Lipschitz continuity of loss functions, as well as its application within various architectures, including convolutional neural networks and federated learning settings. These studies aim to enhance training stability, accelerate convergence, and improve the robustness of implicit regularization, ultimately leading to more efficient and effective deep learning models.