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Batch Normalization (BatchNorm) is a technique that improves the training ofdeep neural networks, especially Convolutional Neural Networks (CNN). It hasbeen empirically demonstrated that BatchNorm increases performance, stability,and accuracy, although the reasons for such improvements are unclear. BatchNormincludes a normalization step as well as trainable shift and scale parameters.In this paper, we empirically examine the relative contribution to the successof BatchNorm of the normalization step, as compared to the re-parameterizationvia shifting and scaling. To conduct our experiments, we implement two newoptimizers in PyTorch, namely, a version of BatchNorm that we refer to asAffineLayer, which includes the re-parameterization step without normalization,and a version with just the normalization step, that we call BatchNorm-minus.We compare the performance of our AffineLayer and BatchNorm-minusimplementations to standard BatchNorm, and we also compare these to the casewhere no batch normalization is used. We experiment with four ResNetarchitectures (ResNet18, ResNet34, ResNet50, and ResNet101) over a standardimage dataset and multiple batch sizes. Among other findings, we provideempirical evidence that the success of BatchNorm may derive primarily fromimproved weight initialization.

An Empirical Analysis of the Shift and Scale Parameters in BatchNorm