Entropy Error-based Convolutional Neural Network Sparse Optimization and Application


  • Kaifang Yang
  • Dewei Yang
  • Fen Wang
  • Jing Zhao
  • Qinwei Fan




Convolution Neural Network (CNN), Smooth L1 Regularization, Cross-Entropy Loss Function, Classification Problems


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.


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How to Cite

Yang, K., Yang, D., Wang, F., Zhao, J., & Fan, Q. (2024). Entropy Error-based Convolutional Neural Network Sparse Optimization and Application. Frontiers in Computing and Intelligent Systems, 7(1), 69-71. https://doi.org/10.54097/rxbm7088