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.
This scenario necessitates the use of composite class labels. In this paper, we
propose a novel framework called Hyper-Evidential Neural Network (HENN)
that explicitly models predictive uncertainty due to composite class 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 results demonstrate
that HENN outperforms its state-of-the-art counterparts based on four image
datasets. The code and datasets are available at: https://github.com/
Hugo101/HyperEvidentialNN.
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Conditional Classification: A Solution for Computational Energy Reduction
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed technique breaks the classification task into two steps: 1) coarse-grain classification, in which the input samples are classified among a set of hyper-classes, 2) fine-grain classification, in which the final labels are predicted among those hyper-classes detected at the first step. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.
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- NSF-PAR ID:
- 10298696
- Date Published:
- Journal Name:
- 22nd International Symposium on Quality Electronic Design (ISQED)
- Page Range / eLocation ID:
- 325 to 330
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISQED51717.2021.9424280
