In this work, we investigate Batch Normalization technique and propose its
probabilistic interpretation. We propose a probabilistic model and show that
Batch Normalization maximazes the lower bound of its marginalized
log-likelihood. Then, according to the new probabilistic model, we design an
algorithm which acts consistently during train and test. However, inference
becomes computationally inefficient. To reduce memory and computational cost,
we propose Stochastic Batch Normalization -- an efficient approximation of
proper inference procedure. This method provides us with a scalable uncertainty
estimation technique. We demonstrate the performance of Stochastic Batch
Normalization on popular architectures (including deep convolutional
architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.

本文提出了对批标准化技术的概率解释，设计了一种概率模型，证明了批标准化可以最大化其较小边界的边际对数似然。基于新的概率模型，我们设计了一种一致性算法，以减少内存和计算成本，并提供了一种可扩展的不确定性估计技术。该方法在 MNIST 和 CIFAR-10 数据集上对受欢迎的架构进行了演示。