Entropy Error-based Convolutional Neural Network Sparse Optimization and Application

Abstract

Convolutional Neural Networks (CNNs), as quintessential representatives of deep neural networks, have found widespread applications in numerous domains such as image recognition, object detection, and image generation, owing to their robust nonlinear mapping capabilities. However, the practical deployment of CNNs faces challenges such as low learning efficiency and subpar accuracy due to the intricacies in the network structure. This paper introduces the smoothing L1 regularization term atop the conventional entropy loss function, effectively enhancing both the learning efficiency and algorithmic precision of Convolutional Neural Networks. The efficacy of the improved algorithm is substantiated through numerical simulations.