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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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Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
In self-supervised monocular depth estimation, the depth discontinuity a...
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Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction
Additionally to the extensive use in clinical medicine, biological age (...
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A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
We propose a new approach for the problem of relative depth estimation f...
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Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
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Evaluation of Neural Network Uncertainty Estimation with Application to Resource-Constrained Platforms
The ability to accurately estimate uncertainties in neural network predi...
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A Simple Probabilistic Model for Uncertainty Estimation
The article focuses on determining the predictive uncertainty of a model...
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The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend to underestimate it. To obtain the predictive uncertainty inherent in an observation, we propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. By decoupling target estimation and uncertainty estimation, we also control the balance between signal estimation and uncertainty estimation. We conduct three types of experiments: regression with simulation data, age estimation, and depth estimation. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
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