基于残差 SDE-Net 的深度神经网络不确定性估计

The neural stochastic differential equation model (SDE-Net) can quantify epistemic uncertainties of deep neural networks (DNNs) from the perspective of a dynamical system. However, SDE-Net faces two problems. Firstly, when dealing with largescale datasets, performance degrades as network layers increase. Secondly, SDE-Net has poor performance in dealing with aleatoric uncertainties caused by in-distribution data with noise or a high missing rate. In order to achieve consistent stability and higher performance, this paper first designs a residual SDE-Net (ResSDE-Net) model, which enhances the residual blocks in residual networks (ResNets). next, convolutional conditional neural processes (ConvCNPs) with translation equivariance are introduced to complete in-distribution data that has noise or a high rate of missing data in order to enhance the ResSDE-Net's processing ability for such datasets. The experimental results demonstrate that the ResSDE-Net performs consistently and predictably when dealing with in-distribution and out-of-distribution data. Additionally, the model still achieves an average accuracy of 89.89%, 65.22%, and 93.02% on the real-world SVHN datasets and the MNIST, CIFAR10, and CIFAR10 datasets, where 70% of the pixels are lost, respectively.