  Augmentations are the key factor in determining the performance of any neural
network as they provide a model with a critical edge in boosting its
performance. Their ability to boost a model's robustness depends on two
factors, viz-a-viz, the model architecture, and the type of augmentations.
Augmentations are very specific to a dataset, and it is not imperative that all
kinds of augmentation would necessarily produce a positive effect on a model's
performance. Hence there is a need to identify augmentations that perform
consistently well across a variety of datasets and also remain invariant to the
type of architecture, convolutions, and the number of parameters used. Hence
there is a need to identify augmentations that perform consistently well across
a variety of datasets and also remain invariant to the type of architecture,
convolutions, and the number of parameters used. This paper evaluates the
effect of parameters using 3x3 and depth-wise separable convolutions on
different augmentation techniques on MNIST, FMNIST, and CIFAR10 datasets.
Statistical Evidence shows that techniques such as Cutouts and Random
horizontal flip were consistent on both parametrically low and high
architectures. Depth-wise separable convolutions outperformed 3x3 convolutions
at higher parameters due to their ability to create deeper networks.
Augmentations resulted in bridging the accuracy gap between the 3x3 and
depth-wise separable convolutions, thus establishing their role in model
generalization. At higher number augmentations did not produce a significant
change in performance. The synergistic effect of multiple augmentations at
higher parameters, with antagonistic effect at lower parameters, was also
evaluated. The work proves that a delicate balance between architectural
supremacy and augmentations needs to be achieved to enhance a model's
performance in any given deep learning task.
