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Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification
Fast estimates of model uncertainty are required for many robust robotic...
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Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved...
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Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds
Deep learning models are extensively used in various safety critical app...
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On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open probl...
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Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Uncertainty and confidence have been shown to be useful metrics in a wid...
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Can Your AI Differentiate Cats from Covid-19? Sample Efficient Uncertainty Estimation for Deep Learning Safety
Deep Neural Networks (DNNs) are known to make highly overconfident predi...
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The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
We propose a new optimization framework for aleatoric uncertainty estima...
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Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in this study, we introduce the deep ensemble equivalent score (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of only few independently trained networks in terms of test performance.
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