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

A Novel Noise Injection-based Training Scheme for Better Model Robustness