KN-RUE: Key Nodes based Resampling Uncertainty Estimation

With the continuous development and advancement of neural networks, in the application of neural networks, users not only require neural networks to be able to complete a given task but also want to know when they can trust the network's prediction results and when they need to be cautious about the prediction results. In response to the need for uncertainty estimation of neural networks, many researchers have invested in the study of uncertainty estimation. Existing uncertainty evaluation methods are difficult to apply to deep neural networks with large parameter scales, complex internal structures, and mappings between inputs and outputs that are hard to express. This paper proposes a key nodes based resampling uncertainty estimation method ((KN-RUE), which achieves uncertainty estimation of prediction results for arbitrarily given large-scale neural networks. In this method, the first step involves analyzing the differences in feature space between adversarial and clean samples, identifying the main nodes affected by adversarial samples, and determining the critical nodes within the network. Next, by resampling the parameters of key nodes, the model is extended while ensuring model performance as much as possible, thus completing the measurement of uncertainty in prediction results. Through experiments, the effectiveness of the extended model and the superiority of uncertainty estimation performance in KN-RUE have been verified.