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Efficient probability intervals for classification using inductive venn predictors
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DOI：10.1016/j.patcog.2023.109734.png)
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摘要
En 中文
Learning enabled components are frequently used by autonomous systems and it is common for deep neural networks to be integrated in such systems for their ability to learn complex, non-linear data pat-terns and make accurate predictions in dynamic environments. However, their large number of parame-ters and their use as black boxes introduce risks as the confidence in each prediction is unknown and out-put values like softmax scores are not usually well-calibrated. Different frameworks have been proposed to compute accurate confidence measures along with the predictions but at the same time introduce a number of limitations like execution time overhead or inability to be used with high-dimensional data. In this paper, we use the Inductive Venn Predictors framework for computing probability intervals regarding the correctness of each prediction in real-time. We propose taxonomies based on distance metric learning to compute informative probability intervals in applications involving high-dimensional inputs. By assign-ing pseudo-labels to unlabeled input data during system deployment we further improve the efficiency of the computed probability intervals. Empirical evaluation on image classification and botnet attacks de-tection in Internet-of-Things (IoT) applications demonstrates improved accuracy and calibration. The pro-posed method is computationally efficient, and therefore, can be used in real-time. The code is available at https://github.com/dboursinos/Efficient-Probability-Intervals-Classification-Inductive-Venn-Predictors .& COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword:
Deep neural networks
Assurance monitoring
Inductive Venn predictors
Probability intervals
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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