  With the complexity of the network structure, uncertainty inference has
become an important task to improve the classification accuracy for artificial
intelligence systems. For image classification tasks, we propose a structured
DropConnect (SDC) framework to model the output of a deep neural network by a
Dirichlet distribution. We introduce a DropConnect strategy on weights in the
fully connected layers during training. In test, we split the network into
several sub-networks, and then model the Dirichlet distribution by match its
moments with the mean and variance of the outputs of these sub-networks. The
entropy of the estimated Dirichlet distribution is finally utilized for
uncertainty inference. In this paper, this framework is implemented on LeNet$5$
and VGG$16$ models for misclassification detection and out-of-distribution
detection on MNIST and CIFAR-$10$ datasets. Experimental results show that the
performance of the proposed SDC can be comparable to other uncertainty
inference methods. Furthermore, the SDC is adapted well to different network
structures with certain generalization capabilities and research prospects.
