mlsanityData to 13 Oct 2026

SLURP: Side Learning Uncertainty for Regression Problems

arXiv 2110.11182 ↗First posted 21 Oct 2021cs.CV cs.LGcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 21 Oct 2021.

Builds on

28 of 51 references.

References · 28

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16 more
  1. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 782
  2. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 380
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 0; 2025: 3; 2026: 1.

12 papers.

  1. 2022MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and taskscited by 24
  2. 2022Latent Discriminant deterministic Uncertaintycited by 17
  3. 2023On the Calibration of Human Pose Estimationcited by 10
  4. 2025Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelscited by 9
  5. 2023ProbVLM: Probabilistic Adapter for Frozen Vision-Language Modelscited by 7
  6. 2023Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regressioncited by 7
  7. 2022Improving the Reliability for Confidence Estimationcited by 6
  8. 2022On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regressioncited by 2
  9. 2025A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimationcited by 2
  10. 2023Measuring and Modeling Uncertainty Degree for Monocular Depth Estimationcited by 1
  11. 2025Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localizationcited by 1
  12. 2026From Misclassifications to Outliers: Joint Reliability Assessment in Classificationcited by 0