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Unimodal probability distributions for deep ordinal classification

arXiv 1705.05278 ↗First posted 15 May 2017stat.MLcited by 28

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Abstract (arXiv)

Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate this approach in the context of deep learning on two large ordinal image datasets, obtaining promising results.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2017.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 7; 2024: 6; 2025: 3; 2026: 7.

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  1. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
  2. 2023Ord2Seq: Regarding Ordinal Regression as Label Sequence Predictioncited by 18
  3. 2022Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimationcited by 17
  4. 2023CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Predictioncited by 16
  5. 2021Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptationcited by 10
  6. 2023Unimodal Distributions for Ordinal Regressioncited by 9
  7. 2023Robust Image Ordinal Regression with Controllable Image Generationcited by 8
  8. 2022Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labelscited by 7
  9. 2021Meta ordinal weighting net for improving lung nodule classificationcited by 4
  10. 2024Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRIcited by 4
  11. 2024Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learningcited by 4
  12. 2024Fusing Echocardiography Images and Medical Records for Continuous Patient Stratificationcited by 3
16 more
  1. 2025OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLMcited by 3
  2. 2023Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learningcited by 2
  3. 2024Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniquescited by 2
  4. 2024Remarks on Loss Function of Threshold Method for Ordinal Regression Problemcited by 2
  5. 2024Learning Ordinality in Semantic Segmentationcited by 1
  6. 2025Approximately Unimodal Likelihood Models for Ordinal Regressioncited by 1
  7. 2026Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Scorecited by 1
  8. 2021Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectoriescited by 0
  9. 2023CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimationcited by 0
  10. 2025Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regressioncited by 0
  11. 2026Conveyance: A Versatile Framework for Learning in Structured Class Spacescited by 0
  12. 2026Demographic Metadata as Construct-Irrelevant Noise in DistilBERT-Based Automated Essay Scoringcited by 0
  13. 2026Deep Neural Networks with Ordinal Loss for Medical Applicationscited by 0
  14. 2026D3O: Dynamic Distribution Distillation for Ordinal Regressioncited by 0
  15. 2026Unimodality-Promoting Regularized Learning for Ordinal Regressioncited by 0
  16. 2026Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Modelscited by 0