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Recently, various normalization layers have been proposed to stabilize thetraining of deep neural networks. Among them, group normalization is ageneralization of layer normalization and instance normalization by allowing adegree of freedom in the number of groups it uses. However, to determine theoptimal number of groups, trial-and-error-based hyperparameter tuning isrequired, and such experiments are time-consuming. In this study, we discuss areasonable method for setting the number of groups. First, we find that thenumber of groups influences the gradient behavior of the group normalizationlayer. Based on this observation, we derive the ideal number of groups, whichcalibrates the gradient scale to facilitate gradient descent optimization. Ourproposed number of groups is theoretically grounded, architecture-aware, andcan provide a proper value in a layer-wise manner for all layers. The proposedmethod exhibited improved performance over existing methods in numerous neuralnetwork architectures, tasks, and datasets.

On the Ideal Number of Groups for Isometric Gradient Propagation