Structured Dropconnect for Uncertainty Inference in Image Classification
Wenqing Zheng, Jiyang Xie, Xian Sun, Zhanyu Ma · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
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 sub-networks. 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.