# A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks. (arXiv:2401.13751v1 [cs.LG])


Convolutional neural networks have shown to be widely applicable to a large
number of fields when large amounts of labelled data are available. The recent
trend has been to use models with increasingly larger sets of tunable parameters
to increase model accuracy, reduce model loss, or create more adversarially
robust models -- goals that are often at odds with one another. In particular,
recent theoretical work raises questions about the ability for even larger
models to generalize to data outside of the controlled train and test sets. As
such, we examine the role of the number of hidden layers in

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