Batch Normalization (BN) is ubiquitously employed for accelerating neural
network training and improving the generalization capability by performing
standardization within mini-batches. Decorrelated Batch Normalization (DBN)
further boosts the above effectiveness by whitening. However, DBN relies
heavily on either a large batch size, or eigen-decomposition that suffers from
poor efficiency on GPUs. We propose Iterative Normalization (IterNorm), which
employs Newton's iterations for much more efficient whitening, while
simultaneously avoiding the eigen-decomposition. Furthermore, we develop a
comprehensive study to show IterNorm has better trade-off between optimization
and generalization, with theoretical and experimental support. To this end, we
exclusively introduce Stochastic Normalization Disturbance (SND), which
measures the inherent stochastic uncertainty of samples when applied to
normalization operations. With the support of SND, we provide natural
explanations to several phenomena from the perspective of optimization, e.g.,
why group-wise whitening of DBN generally outperforms full-whitening and why
the accuracy of BN degenerates with reduced batch sizes. We demonstrate the
consistently improved performance of IterNorm with extensive experiments on
CIFAR-10 and ImageNet over BN and DBN.

本文提出了一种新的神经网络训练方法：Iterative Normalization，通过牛顿迭代实现高效且无矩阵分解的白化，同时引入 Stochastic Normalization Disturbance，增加实验的稳定性，实验表明 IterNorm 具有更好的优化与泛化折衷。