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Normalization techniques play an important role in supporting efficient andoften more effective training of deep neural networks. While conventionalmethods explicitly normalize the activations, we suggest to add a loss terminstead. This new loss term encourages the variance of the activations to bestable and not vary from one random mini-batch to the next. As we prove, thisencourages the activations to be distributed around a few distinct modes. Wealso show that if the inputs are from a mixture of two Gaussians, the new losswould either join the two together, or separate between them optimally in theLDA sense, depending on the prior probabilities. Finally, we are able to linkthe new regularization term to the batchnorm method, which provides it with aregularization perspective. Our experiments demonstrate an improvement inaccuracy over the batchnorm technique for both CNNs and fully connectednetworks.

Regularizing by the Variance of the Activations' Sample-Variances