Unified Normalization

Unified normalization in deep learning aims to improve the efficiency and stability of neural network training and inference by developing normalization techniques applicable across diverse architectures and tasks. Current research focuses on developing novel normalization methods, such as adaptive fusion normalization and weight conditioning, that outperform existing techniques like Layer Normalization and Batch Normalization, particularly in addressing issues like slow convergence, bias amplification, and hardware limitations. These advancements are significant because they enhance the performance and efficiency of various deep learning models, impacting applications ranging from image classification and speech recognition to robotics and graph neural networks.