Papers › Norm matters: efficient and accurate normalization schemes in deep networks

Norm matters: efficient and accurate normalization schemes in deep networks

5 Mar 2018NeurIPS 2018 12arXiv:1803.01814archive 2025-07-28

Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry

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. This property highlights the connection between practices such as normalization, weight decay and learning-rate adjustments. We suggest several alternatives to the widely used L² batch-norm, using normalization in L¹ and L^∞ 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. Finally, we suggest a modification to weight-normalization, which improves its performance on large-scale tasks.

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