The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals

• URL: http://arxiv.org/abs/2011.01655v1
• Date: Tue, 3 Nov 2020 12:11:27 GMT
• Title: The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
• Authors: Takumi Kawashima and Qing Yu and Akari Asai and Daiki Ikami and Kiyoharu Aizawa
• Abstract summary: Existing methods can quantify the error in the target estimation, but they tend to underestimate it. We propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting. We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
• Score: 51.71066839337174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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