New framework unifies CNN information mechanics with physics equations

This paper introduces a unified mathematical framework to model information propagation within convolutional neural networks (CNNs), aiming to bridge the gap between physical and information spaces. It establishes a correspondence between discrete filter symmetry in CNNs and relativistic energy-momentum relations, where symmetric components act like rest energy and antisymmetric components like momentum. The framework demonstrates how repeated filtering leads to Gaussian scale-space and scale-invariant features, sharing structural similarities with the heat, Schrödinger, and Friedmann equations, and revealing emergent Morse topological structures in various physical scales. AI

IMPACT This theoretical framework could lead to new insights into how CNNs process information, potentially improving their interpretability and efficiency in image analysis tasks.

RANK_REASON The item is an academic paper published on arXiv detailing a new theoretical framework for CNNs. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• CNN
• convolutional neural network
• cosmic microwave background
• Friedmann equations
• Gaussian Scale-Space Enhanced Local Contrast Measure for Small Infrared Target Detection
• heat equation
• Laplace operator
• magnetic resonance imaging
• Morse
• Schrödinger equation

• paper
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