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- W4308236202 abstract "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." @default.
- W4308236202 created "2022-11-09" @default.
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- W4308236202 date "2022-10-16" @default.
- W4308236202 modified "2023-10-14" @default.
- W4308236202 title "Structured Dropconnect for Uncertainty Inference in Image Classification" @default.
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- W4308236202 doi "https://doi.org/10.1109/icip46576.2022.9897405" @default.
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