mlsanityData to 14 Oct 2026

Structured DropConnect for Uncertainty Inference in Image Classification

arXiv 2106.08624 ↗First posted 16 Jun 2021cs.CV cs.AIcited by 1

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Abstract (arXiv)

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 LeNet5 and VGG16 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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

19 of 33 references.

References · 19

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7 more
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