BraidNet: procedural generation of neural networks for image classification problems using braid theory
This is an incremental improvement for image classification tasks, offering a novel procedural generation method.
The authors tackled neural network optimization for image classification by combining information theory and braid theory to create BraidNet, which showed advantages in learning speed and classification accuracy compared to simplified networks.
In this article, we propose the approach to procedural optimization of a neural network, based on the combination of information theory and braid theory. The network studied in the article implemented with the intersections between the braid strands, as well as simplified networks (a network with strands without intersections and a simple convolutional deep neural network), are used to solve various problems of multiclass image classification that allow us to analyze the comparative effectiveness of the proposed architecture. The simulation results showed BraidNet's comparative advantage in learning speed and classification accuracy.