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Controlling Covariate Shift using Balanced Normalization of Weights

Machine Learning 2019-05-13 v2 Machine Learning

Abstract

We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. The proposed technique keeps the contribution of positive and negative weights to the layer output balanced. We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.

Keywords

Cite

@article{arxiv.1812.04549,
  title  = {Controlling Covariate Shift using Balanced Normalization of Weights},
  author = {Aaron Defazio and Léon Bottou},
  journal= {arXiv preprint arXiv:1812.04549},
  year   = {2019}
}
R2 v1 2026-06-23T06:39:15.571Z