The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks

Normalization methods play a critical role in deep neural networks by stabilizing the optimization process and improving generalization, as highlighted in recent research. These techniques are widely recognized for their practical benefits in training deep models, yet the theoretical understanding of how they achieve these effects remains incomplete. This gap in knowledge is particularly evident when considering architectures that incorporate multiple normalization layers, where the underlying mechanisms are not fully elucidated. The article from arXiv emphasizes the importance of further investigation into these theoretical aspects to better comprehend the exponential capacity control enabled by normalization. Such insights could potentially lead to more effective design and training of deep neural networks. Overall, normalization continues to be a foundational component in deep learning, with ongoing research aimed at uncovering the principles behind its success.