The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals

  title={The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals},
  author={Takumi Kawashima and Qing Yu and Akari Asai and Daiki Ikami and Kiyoharu Aizawa},
  journal={2020 25th International Conference on Pattern Recognition (ICPR)},
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… 
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