  Deep Neural Networks (DNNs) have begun to thrive in the field of automation
systems, owing to the recent advancements in standardising various aspects such
as architecture, optimization techniques, and regularization. In this paper, we
take a step towards a better understanding of Spectral Normalization (SN) and
its potential for standardizing regularization of a wider range of Deep
Learning models, following an empirical approach. We conduct several
experiments to study their training dynamics, in comparison with the ubiquitous
Batch Normalization (BN) and show that SN increases the gradient sparsity and
controls the gradient variance. Furthermore, we show that SN suffers from a
phenomenon, we call the mean-drift effect, which mitigates its performance. We,
then, propose a weight reparameterization called as the Mean Spectral
Normalization (MSN) to resolve the mean drift, thereby significantly improving
the network's performance. Our model performs ~16% faster as compared to BN in
practice, and has fewer trainable parameters. We also show the performance of
our MSN for small, medium, and large CNNs - 3-layer CNN, VGG7 and DenseNet-BC,
respectively - and unsupervised image generation tasks using Generative
Adversarial Networks (GANs) to evaluate its applicability for a broad range of
embedded automation tasks.
