A Unified Framework for the Mechanics of Information in Convolutional Neural Network Image Space
This paper introduces a unified mathematical framework for modeling information propagation through convolutional neural networks (CNNs), with the aim of connecting descriptions of physical space and information space. A correspondence is presented linking discrete filter symmetry and the relativistic energy--momentum relation under the widely used nonlinear rectified convolution operation. Specifically, symmetric filter components (e.g. the sum $Σ= [1,1]$) operate analogously to rest energy $mc^2$ in preserving the image centre of mass (e.g. isotropic diffusion), whereas antisymmetric components (e.g. the gradient $\nabla = [-1,1]$) operate analogously to the momentum term $pc$ in genera...
| Authors | Aryan Shukla, Matthew Toews |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 31 August 2026 |
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