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Normalization operations are essential for state-of-the-art neural networksand enable us to train a network from scratch with a large learning rate (LR).We attempt to explain the real effect of Batch Normalization (BN) from theperspective of variance transmission by investigating the relationship betweenBN and Weights Normalization (WN). In this work, we demonstrate that theproblem of the shift of the average gradient will amplify the variance of everyconvolutional (conv) layer. We propose Parametric Weights Standardization(PWS), a fast and robust to mini-batch size module used for conv filters, tosolve the shift of the average gradient. PWS can provide the speed-up of BN.Besides, it has less computation and does not change the output of a convlayer. PWS enables the network to converge fast without normalizing theoutputs. This result enhances the persuasiveness of the shift of the averagegradient and explains why BN works from the perspective of variancetransmission. The code and appendix will be made available onhttps://github.com/lyxzzz/PWSConv.

Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse