Two-Step Color-Polarization Demosaicking Network

Uncertainty inference has become an important task to prove the reliability for deep neural networks. For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network into a distribution. We introduce a DropConnect strategy in a structured manner in the fully connected layers, split the network into several sub-networks during testing, and choose the Dirichlet distribution to model the outputs of these subnetworks. The entropy of the parameterized Dirichlet distribution is finally utilized for uncertainty inference. in this paper, this framework is implemented on VGG16 and ResNet18 models for misclassification detection and open-set out-of-domain detection on CIFAR-10 and CIFAR-100 datasets. Experimental results show that the performance of the proposed SDC can be comparable to other uncertainty inference methods.