Normalization Layer

Normalization layers are crucial components in deep neural networks, primarily aiming to stabilize training and improve generalization by addressing issues like internal covariate shift and exploding gradients. Current research focuses on optimizing normalization layer design for specific architectures (e.g., transformers, graph neural networks, residual networks) and addressing challenges in continual learning and federated learning settings, including mitigating recency bias and handling non-IID data. These advancements are significant because improved normalization techniques lead to more robust and efficient training of deep learning models across diverse applications, from image classification and object detection to financial prediction and medical image analysis.