Training neural networks is an optimization problem, and finding a decent set of parameters through gradient descent can be a difficult task. A host of techniques has been developed to aid this process before and during the training phase. One of the most important and widely used class of method is normalization. It is generally favorable for neurons to receive inputs that are distributed with zero mean and unit variance, so we use statistics about dataset to normalize them before the first layer. However, this property cannot be guaranteed for the intermediate activations inside the network. A widely used method to enforce this property inside the network is batch normalization. It was developed to combat covariate shift inside networks. Empirically it is known to work, but there is a lack of theoretical understanding about its effectiveness and potential drawbacks it might have when used in practice. This work studies batch normalization in detail, while comparing it with other methods such as weight normalization, gradient clipping and dropout. The main purpose of this work is to determine if it is possible to train networks effectively when batch normalization is removed through adaption of the training process.

本篇论文详细研究了批量归一化在训练神经网络中的作用，以及其与其他优化方法的比较，主要目的是通过改进训练过程判断是否有可能在不使用批量归一化情况下有效地训练网络。

无批归一化训练深度神经网络