# GM-PLL: Graph Matching based Partial Label Learning

arXiv: https://arxiv.org/abs/1901.03073 · HTML: https://mlsanity.com/paper/1901.03073/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 23 of 23)

- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning](https://mlsanity.com/paper/2209.10365/) (2022)
- [PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning](https://mlsanity.com/paper/2201.08984/) (2022)
- [On the Robustness of Average Losses for Partial-Label Learning](https://mlsanity.com/paper/2106.06152/) (2021)
- [Realistic Evaluation of Deep Partial-Label Learning Algorithms](https://mlsanity.com/paper/2502.10184/) (2025)
- [Towards Effective Visual Representations for Partial-Label Learning](https://mlsanity.com/paper/2305.06080/) (2023)
- [Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2305.16912/) (2023)
- [Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision](https://mlsanity.com/paper/2212.08997/) (2022)
- [Rethinking the Learning Paradigm for Facial Expression Recognition](https://mlsanity.com/paper/2209.15402/) (2022)
- [IRNet: Iterative Refinement Network for Noisy Partial Label Learning](https://mlsanity.com/paper/2211.04774/) (2022)
- [Unreliable Partial Label Learning with Recursive Separation](https://mlsanity.com/paper/2302.09891/) (2023)
- [Complementary Classifier Induced Partial Label Learning](https://mlsanity.com/paper/2305.09897/) (2023)
- [A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition](https://mlsanity.com/paper/2305.12485/) (2023)
- [Controller-Guided Partial Label Consistency Regularization with Unlabeled Data](https://mlsanity.com/paper/2210.11194/) (2022)
- [Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2512.17788/) (2025)
- [CLIP-driven Zero-shot Learning with Ambiguous Labels](https://mlsanity.com/paper/2603.05053/) (2026)
- [Adversary-Aware Partial label learning with Label distillation](https://mlsanity.com/paper/2304.00498/) (2023)
- [Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples](https://mlsanity.com/paper/2307.00553/) (2023)
- [AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning](https://mlsanity.com/paper/2407.15036/) (2024)
- [Towards Robust Incremental Learning under Ambiguous Supervision](https://mlsanity.com/paper/2501.13584/) (2025)
- [Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning](https://mlsanity.com/paper/2505.09354/) (2025)
- [Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation](https://mlsanity.com/paper/2506.03229/) (2025)
- [Combating Noisy Labels through Fostering Self- and Neighbor-Consistency](https://mlsanity.com/paper/2601.12795/) (2026)

## Abstract (arXiv)

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods.