mlsanityData to 14 Oct 2026

Progressive Identification of True Labels for Partial-Label Learning

arXiv 2002.08053 ↗First posted 19 Feb 2020cs.LG stat.MLcited by 102

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Abstract (arXiv)

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

Key facts. Cited by 102 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 16; 2023: 27; 2024: 16; 2025: 19; 2026: 15.

102 papers.

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  1. 2021Leveraged Weighted Loss for Partial Label Learningcited by 66
  2. 2021Instance-Dependent Partial Label Learningcited by 49
  3. 2022SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learningcited by 31
  4. 2023S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captionscited by 30
  5. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
  6. 2022Progressive Purification for Instance-Dependent Partial Label Learningcited by 23
  7. 2022One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancementcited by 22
  8. 2023Long-Tailed Partial Label Learning via Dynamic Rebalancingcited by 22
  9. 2021Efficient Algorithms for Learning from Coarse Labelscited by 21
  10. 2022PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learningcited by 21
  11. 2021On the Robustness of Average Losses for Partial-Label Learningcited by 20
  12. 2021Learning from Similarity-Confidence Datacited by 19
38 more
  1. 2023On Learning Latent Models with Multi-Instance Weak Supervisioncited by 13
  2. 2021Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classificationcited by 12
  3. 2025Realistic Evaluation of Deep Partial-Label Learning Algorithmscited by 12
  4. 2023CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learningcited by 11
  5. 2023Towards Effective Visual Representations for Partial-Label Learningcited by 11
  6. 2023Disambiguated Attention Embedding for Multi-Instance Partial-Label Learningcited by 10
  7. 2023Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practicalcited by 10
  8. 2022Learning with Partial Labels from Semi-supervised Perspectivecited by 9
  9. 2022Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervisioncited by 9
  10. 2023ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learningcited by 9
  11. 2023Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurationscited by 9
  12. 2023Active Learning for Semantic Segmentation with Multi-class Label Querycited by 9
  13. 2021Learning with Proper Partial Labelscited by 8
  14. 2023Learning From Biased Soft Labelscited by 8
  15. 2022Class-Imbalanced Complementary-Label Learning via Weighted Losscited by 7
  16. 2024A General Framework for Learning from Weak Supervisioncited by 7
  17. 2024Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learningcited by 7
  18. 2023Weakly Supervised Regression with Interval Targetscited by 6
  19. 2024Tuning Vision-Language Models with Candidate Labels by Prompt Alignmentcited by 6
  20. 2023Conformal Prediction with Partially Labeled Datacited by 5
  21. 2023Unified Risk Analysis for Weakly Supervised Learningcited by 5
  22. 2025Multi-Instance Partial-Label Learning with Margin Adjustmentcited by 5
  23. 2021Multi-Class Classification from Single-Class Data with Confidencescited by 3
  24. 2022Rethinking the Learning Paradigm for Facial Expression Recognitioncited by 3
  25. 2022IRNet: Iterative Refinement Network for Noisy Partial Label Learningcited by 3
  26. 2023Unreliable Partial Label Learning with Recursive Separationcited by 3
  27. 2023Partial Label Learning for Emotion Recognition from EEGcited by 3
  28. 2023A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognitioncited by 3
  29. 2023Partial-Label Regressioncited by 3
  30. 2024Partial-Label Learning with a Reject Optioncited by 3
  31. 2024Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learningcited by 3
  32. 2022Learning from Label Proportions with Instance-wise Consistencycited by 2
  33. 2022Controller-Guided Partial Label Consistency Regularization with Unlabeled Datacited by 2
  34. 2023Robust Representation Learning for Unreliable Partial Label Learningcited by 2
  35. 2024Learning from Reduced Labels for Long-Tailed Datacited by 2
  36. 2024Imbalances in Neurosymbolic Learning: Characterization and Mitigating Strategiescited by 2
  37. 2024Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportionscited by 2
  38. 2025Partial-Label Learning with Conformal Candidate Cleaningcited by 2

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