Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty

Changbin Li, Kangshuo Li, Yuzhe Ou, Lance M. Kaplan, Audun Jøsang, Jin-Hee Cho, DONG HYUN JEONG, Feng Chen

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Keywords: Evidential Neural Network, hyperdomain, vagueness

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TL;DR: We propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty caused by composite set labels in training data using a belief theory called Subjective Logic (SL).

Abstract:

Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. When an image is ambiguous, such as a blurry one where an annotator can't distinguish between a husky and a wolf, it may be labeled with both classes: {husky, wolf}. This scenario necessitates the use of composite set labels.
In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty caused by composite set labels in training data in the context of the belief theory called Subjective Logic (SL).
By placing a Grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data.
We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs.
Our experiments prove that HENN outperforms its state-of-the-art counterparts based on four image datasets.
The code and datasets are available at: https://shorturl.at/dhoqx.

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Primary Area: general machine learning (i.e., none of the above)

Submission Number: 6833