Norm matters: efficient and accurate normalization schemes in deep networks

Code

• github.com/eladhoffer/norm_matters
  OfficialIn paperpytorch★ 0
• github.com/Abhimanyu08/L-1_BatchNorm
  pytorch★ 0
• github.com/eladhoffer/convNet.pytorch
  pytorch★ 0

Abstract

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 L^2 batch-norm, using normalization in L^1 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.

Reproductions