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SLURP: Side Learning Uncertainty for Regression Problems
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It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results. Uncertainty estimation for regression has received less attention than classification due to the more straightforward standardized output of the latter class of tasks and their high importance. However, regression problems are encountered in a wide range of applications in computer vision. We propose SLURP, a generic approach for regression uncertainty estimation via a side learner that exploits the output and the intermediate representations generated by the main task model. We test SLURP on two critical regression tasks in computer vision: monocular depth and optical flow estimation. In addition, we conduct exhaustive benchmarks comprising transfer to different datasets and the addition of aleatoric noise. The results show that our proposal is generic and readily applicable to various regression problems and has a low computational cost with respect to existing solutions.
Forward citations
Cited by 2 Pith papers
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A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
Fine-tuning DepthAnythingV2 with a Gaussian negative log-likelihood loss yields the most reliable pixel-wise uncertainty estimates on indoor, street, and object scenes, but it fails on aerial large-depth data.
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