标题： 内部噪声对卷积神经网络的影响 显示英文标题

标题： Impact of internal noise on convolutional neural networks

摘要： 本文中，我们研究了噪声对简化训练的卷积网络的影响。研究的噪声类型源自神经网络的实际光学实现，但我们将其推广以增强我们的发现对更大范围的应用性。考虑的噪声类型包括加性噪声和乘性噪声，这些噪声与噪声如何影响单个神经元有关，以及相关噪声和非相关噪声，这些噪声涉及到噪声在一层中的影响。我们证明非相关噪声的传播主要依赖于连接矩阵的统计特性。具体来说，受噪声影响层之后的连接矩阵的均值控制了相关加性噪声的传播，而其平方的均值则有助于非相关噪声的积累。此外，我们提出了对网络输出信号的噪声水平进行分析评估的方法，该方法显示出与数值模拟结果有很强的相关性。 显示英文摘要

摘要： In this paper, we investigate the impact of noise on a simplified trained convolutional network. The types of noise studied originate from a real optical implementation of a neural network, but we generalize these types to enhance the applicability of our findings on a broader scale. The noise types considered include additive and multiplicative noise, which relate to how noise affects individual neurons, as well as correlated and uncorrelated noise, which pertains to the influence of noise across one layers. We demonstrate that the propagation of uncorrelated noise primarily depends on the statistical properties of the connection matrices. Specifically, the mean value of the connection matrix following the layer impacted by noise governs the propagation of correlated additive noise, while the mean of its square contributes to the accumulation of uncorrelated noise. Additionally, we propose an analytical assessment of the noise level in the network's output signal, which shows a strong correlation with the results of numerical simulations.