What Are The Alternatives To Batch Normalization In Deep Learning?

In the original BatchNorm paper, the authors Sergey Ioffe and Christian Szegedy of Google introduced a method to address a phenomenon called internal covariate shift. This occurs because the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialisation. This makes training the models harder. The introduction of batch normalized networks helped achieve state-of-the-art accuracies with 14 times fewer training steps.