Over the past few years batch-normalization has been commonly used in deep networks, allowing faster training and high performance for a wide variety of applications. However, the reasons behind its merits remained unanswered, with several shortcomings that hindered its use for certain tasks. In this work we present a novel view on the purpose and function of normalization methods and weight-decay, as tools to decouple weights' norm from the underlying optimized objective. We also improve the use of weight-normalization and show the connection between practices such as normalization, weight decay and learning-rate adjustments. Finally, we suggest several alternatives to the widely used $L^2$ batch-norm, using normalization in $L^1$ and $L^\infty$ spaces that can substantially improve numerical stability in low-precision implementations as well as provide computational and memory benefits. We demonstrate that such methods enable the first batch-norm alternative to work for half-precision implementations.

本研究提出了一种新的对归一化方法和权值衰减的目的和功能的视角，并建议了几种常用L2批归一化的替代方案，包括L1和L∞范数中的归一化，以提高计算和内存效率，并且在低精度实现中大幅提高了数值稳定性，并且还提出了一种改进重要性重归一化的方法，可以提高其在大规模任务上的性能。

规范化重要性：深度神经网络中高效准确的规范化方法