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Augmentations: an Insight into Their Effectiveness on Convolution Neural Networks

EasyChair Preprint no. 7901

12 pagesDate: May 4, 2022


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. This paper evaluates the effect of parameters and depthwise 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. The effect of parameterization was superior to that of augmentation since strongly parameterized models remained invariant to various augmentations. The synergistic effect of multiple augmentations at higher parameters, with antagonistic effect at lower parameters, was also shown. 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

Keyphrases: Augmentation paradox, Augmentations, Cutouts, deep learning, Depth-wise Separable convolutions, Global Average Pooling, Mixup

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sabeesh Ethiraj and Bharath Bolla},
  title = {Augmentations: an Insight into Their Effectiveness on Convolution Neural Networks},
  howpublished = {EasyChair Preprint no. 7901},

  year = {EasyChair, 2022}}
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