Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-based training scheme for better model robustness. Specifically, we first develop a likelihood ratio method to estimate the gradient with respect to both synaptic weights and noise levels for stochastic gradient descent training. Then, we design an approximation for the vanilla noise injection-based training method to reduce memory and improve computational efficiency. Next, we apply our proposed scheme to spiking neural networks and evaluate the performance of classification accuracy and robustness on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed method achieves a much better performance on adversarial robustness and slightly better performance on original accuracy, compared with the conventional gradient-based training method.

翻译：先前研究表明，基于噪声注入的方法能够提升人工神经网络的鲁棒性。本文提出一种基于噪声注入的新型训练方案，旨在进一步增强模型鲁棒性。具体而言，我们首先开发了一种似然比方法，用于在随机梯度下降训练中估算关于突触权重和噪声水平的梯度。随后，我们针对原始噪声注入训练方法设计了一种近似算法，以降低内存消耗并提升计算效率。接着，我们将所提方案应用于脉冲神经网络，并在MNIST和Fashion-MNIST数据集上评估了分类精度与鲁棒性性能。实验结果表明，与传统基于梯度的训练方法相比，所提方法在对抗鲁棒性方面实现了显著更优的性能，同时在原始精度上也有小幅提升。