Article Open Access http://dx.doi.org/10.26855/aitcs.2025.06.001
Gradient Disappearance Problem of Deep Learning Model and New Optimization Strategy
Jian Sun1,*, Yizheng Xu2, Yansong Li3
1Iowa State University, Ames, Iowa 50011, USA.
2University of Malaya, Kuala Lumpur 50603, Malaysia.
3Zhengzhou Police College, Zhengzhou 450000, Henan, China.
*Corresponding author: Jian Sun
Published: August 11,2025
Abstract
The rapid development of deep neural networks has led to increasing model depth, but the vanishing gradient problem severely limits training efficiency and performance. This paper systematically analyzes the mathematical mechanism of the vanishing gradient problem, highlighting its exponential gradient decay caused by the chain rule during backpropagation, and discusses its impact across different activation functions (e.g., Sigmoid, ReLU) and architectures (e.g., RNN, CNN). To address this issue, we review traditional optimization strategies (e.g., residual connections, batch normalization, improved optimizers) and their limitations, then propose novel approaches, including attention-based gradient stabilization, differential equation-inspired continuous optimization, and meta-learning-based adaptive tuning. Experiments on datasets such as CIFAR-100 and PTB validate the effectiveness of these methods, supplemented by ablation studies. Finally, we explore future research directions, such as biologically inspired neural mechanisms and quantum computing-accelerated gradient descent. This study provides theoretical insights and practical guidance for stable training of deep learning models, contributing to the optimization of ultra-deep networks.
Keywords
Deep learning; Vanishing gradient; Backpropagation; Optimization strategies; Re-sidual connections; Adaptive optimization
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How to cite this paper
Gradient Disappearance Problem of Deep Learning Model and New Optimization Strategy
How to cite this paper: Jian Sun, Yizheng Xu, Yansong Li. (2025) Gradient Disappearance Problem of Deep Learning Model and New Optimization Strategy. Advance in Information Technology and Computer Science, 2(1), 1-5.
DOI: http://dx.doi.org/10.26855/aitcs.2025.06.001