Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts

• URL: http://arxiv.org/abs/2403.05600v1
• Date: Thu, 7 Mar 2024 23:20:34 GMT
• Title: Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts
• Authors: Manh Ha Bui and Anqi Liu
• Abstract summary: Density-Regression is a method that leverages the density function in uncertainty estimation and achieves fast inference by a single forward pass. We show that Density-Regression has competitive uncertainty estimation performance under distribution shifts with modern deep regressors.
• Score: 11.048463491646993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Morden deep ensembles technique achieves strong uncertainty estimation performance by going through multiple forward passes with different models. This is at the price of a high storage space and a slow speed in the inference (test) time. To address this issue, we propose Density-Regression, a method that leverages the density function in uncertainty estimation and achieves fast inference by a single forward pass. We prove it is distance aware on the feature space, which is a necessary condition for a neural network to produce high-quality uncertainty estimation under distribution shifts. Empirically, we conduct experiments on regression tasks with the cubic toy dataset, benchmark UCI, weather forecast with time series, and depth estimation under real-world shifted applications. We show that Density-Regression has competitive uncertainty estimation performance under distribution shifts with modern deep regressors while using a lower model size and a faster inference speed.

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