Convolutional Neural Network (CNN) Layer Development for Effectiveness of Classification Tasks

Abstract

This paper presents a novel 2D convolutional layer motivated by the principles of Partial Differential Equation (PDE) of Neural Interaction. Our objective is to leverage this layer to enhance the classification accuracy of Deep Convolutional Neural Networks (DCNN) for various classification tasks. We place a particular emphasis on its integration within the ResNet architecture, and we conduct experimental evaluations on the CIFAR10 and STL10 datasets to validate its efficacy.