While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks-- \textit{Internal Covariate Shift}-- the current solution has multiple drawbacks. For instance, BN depends on batch statistics for layerwise input normalization during training which makes the estimates of mean and standard deviation of input (distribution) to hidden layers inaccurate due to shifting parameter values (specially during initial training epochs). Another fundamental problem with BN is that it cannot be used with batch-size $ 1 $ during training. We address these (and other) drawbacks of BN by proposing a non-adaptive normalization technique for removing covariate shift, that we call \textit{Normalization Propagation}. Our approach does not depend on batch statistics, but rather uses a data-independent parametric estimate of mean and standard-deviation in every layer; thus being computationally cheaper. We exploit the observation that the pre-activation before Rectified Linear Units follow a Gaussian distribution in deep networks, and that once the first and second order statistics of any given dataset are normalized, we can forward propagate this normalization without the need for recalculating the approximate statistics (using data) for any of the hidden layers.

介绍了一种用于解决深度神经网络训练中的内部协变量漂移问题的非自适应规范化技术——Normalization Propagation，其利用一种数据独立的参数估计，通过预先规范化激活函数前的数据分布特征，实现了批标准化无法实现的单个Batch的训练，进一步减少了运算复杂性。

标准化传播：一种参数技术用于消除深层网络中的内部协变量移位