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To this aim, we introduce a novel framework that exploits data augmentation for estimating the uncertainty introduced by sensor signals.Our experiments on ...
Through our experiments, we show that augmentation-based uncertainty makes better decisions, as the more informative signals are labelled first and the learner ...
To this aim, we introduce a novel framework that exploits data augmentation for estimating the uncertainty introduced by sensor signals. Our experiments on ...
The proposed general aleatoric uncertainty estimation is formulated in a consistent mathematical framework including two parts. The first part is a mathematical ...
Publications · Uncertainty Estimation with Data Augmentation for Active Learning Tasks on Health Data, July 2023 · Uncertainty Estimation for Sequence-to-Sequence ...
This systematic review offers a thorough overview of deep learning-based data augmentation in medical imaging, covering application domains, models, results ...
These uncertainty quantification schemes provide qualitative information on the reliability of the ML predictions, and are widely used in the context of online ...
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Aug 2, 2022 · To evaluate the impact of the applied uncertainty estimation method, the authors report retained data versus accuracy curves. This means that a ...
Furthermore, probabilistic source models with uncertainty estimation are able to contribute in identifying target data points lying outside the source manifold, ...
Jun 10, 2024 · 1). Leveraging data augmentation techniques effectively introduces variability in the training data, leading to a more robust and general model.