mlsanityData to 7 Oct 2026

Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

arXiv 2103.13629 ↗First posted 25 Mar 2021cs.CV cs.LGcited by 58

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Abstract (arXiv)

Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more popular in practice while the direct regression methods suffer from the limited performance. How to model the uncertainty within the present-day technologies for regression remains an open issue. In this paper, we propose to learn probabilistic ordinal embeddings which represent each data as a multivariate Gaussian distribution rather than a deterministic point in the latent space. An ordinal distribution constraint is proposed to exploit the ordinal nature of regression. Our probabilistic ordinal embeddings can be integrated into popular regression approaches and empower them with the ability of uncertainty estimation. Experimental results show that our approach achieves competitive performance. Code is available at https://github.com/Li-Wanhua/POEs.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 27 of its 61 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

27 of 61 references.

References · 27

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  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
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  6. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
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  11. 2019Probabilistic Face Embeddingscited by 171
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15 more
  1. 2018Bounding Box Regression with Uncertainty for Accurate Object Detectioncited by 143
  2. 2020Data Uncertainty Learning in Face Recognitioncited by 134
  3. 2016Photo Aesthetics Ranking Network with Attributes and Content Adaptationcited by 122
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  9. 2019BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimationcited by 39
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 8; 2024: 19; 2025: 13; 2026: 7.

58 papers.

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  1. 2022Rank-N-Contrast: Learning Continuous Representations for Regressioncited by 74
  2. 2022Moving Window Regression: A Novel Approach to Ordinal Regressioncited by 44
  3. 2022OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regressioncited by 44
  4. 2024FaceXFormer: A Unified Transformer for Facial Analysiscited by 34
  5. 2023DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matricescited by 28
  6. 2022Label2Label: A Language Modeling Framework for Multi-Attribute Learningcited by 23
  7. 2022COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognitioncited by 22
  8. 2023Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classificationcited by 21
  9. 2024Teach CLIP to Develop a Number Sense for Ordinal Regressioncited by 20
  10. 2023Ord2Seq: Regarding Ordinal Regression as Label Sequence Predictioncited by 18
  11. 2024RICA2: Rubric-Informed, Calibrated Assessment of Actionscited by 13
  12. 2025CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Losscited by 11
38 more
  1. 2022Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognitioncited by 9
  2. 2023Robust Image Ordinal Regression with Controllable Image Generationcited by 8
  3. 2024Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Spacecited by 8
  4. 2023Introspective Deep Metric Learningcited by 7
  5. 2024Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformercited by 7
  6. 2024Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammogramscited by 6
  7. 2025A Survey on Ordinal Regression: Applications, Advances and Prospectscited by 5
  8. 2024DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Imagescited by 4
  9. 2024Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regressioncited by 3
  10. 2024Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithmscited by 3
  11. 2024Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Mapscited by 3
  12. 2024Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysiscited by 3
  13. 2024Ordinal-Aware Calibration for Ordinal Classificationcited by 3
  14. 2025IMG: Calibrating Diffusion Models via Implicit Multimodal Guidancecited by 3
  15. 2022Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddingscited by 2
  16. 2022Probabilistic Deep Metric Learning for Hyperspectral Image Classificationcited by 2
  17. 2023Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learningcited by 2
  18. 2023General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stonecited by 2
  19. 2024Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimationcited by 2
  20. 2025TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformercited by 2
  21. 2025OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLMcited by 2
  22. 2025FaceInsight: A Multimodal Large Language Model for Face Perceptioncited by 2
  23. 2025Parameterized Diffusion Optimization enabled Autoregressive Ordinal Regression for Diabetic Retinopathy Gradingcited by 2
  24. 2024Beyond the Known: Adversarial Autoencoders in Novelty Detectioncited by 1
  25. 2024Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrievalcited by 1
  26. 2025ACCon: Angle-Compensated Contrastive Regularizer for Deep Regressioncited by 1
  27. 2025Beyond the Horizon: Decoupling Multi-View UAV Action Recognition via Partial Order Transfercited by 1
  28. 2025Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regressioncited by 1
  29. 2026Learning Ordinal Probabilistic Reward from Preferencescited by 1
  30. 2021Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Modelcited by 0
  31. 2022Introspective Deep Metric Learning for Image Retrievalcited by 0
  32. 2022Angular triangle distance for ordinal metric learningcited by 0
  33. 2023CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimationcited by 0
  34. 2024DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimationcited by 0
  35. 2024Uncertainty-oriented Order Learning for Facial Beauty Predictioncited by 0
  36. 2024Self-Relaxed Joint Training: Sample Selection for Severity Estimation with Ordinal Noisy Labelscited by 0
  37. 2024Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Imagecited by 0
  38. 2025Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Predictioncited by 0

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