# Partial Label Learning with Self-Guided Retraining

arXiv: https://arxiv.org/abs/1902.03045 · HTML: https://mlsanity.com/paper/1902.03045/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 30 of 47)

- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2110.12911/) (2021)
- [Progressive Purification for Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2206.00830/) (2022)
- [Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning](https://mlsanity.com/paper/2312.09505/) (2023)
- [On the Robustness of Average Losses for Partial-Label Learning](https://mlsanity.com/paper/2106.06152/) (2021)
- [Learning with Partial Labels from Semi-supervised Perspective](https://mlsanity.com/paper/2211.13655/) (2022)
- [Personalized Reward Learning with Interaction-Grounded Learning (IGL)](https://mlsanity.com/paper/2211.15823/) (2022)
- [Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision](https://mlsanity.com/paper/2212.08997/) (2022)
- [Learning with Group Noise](https://mlsanity.com/paper/2103.09468/) (2021)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Rethinking the Learning Paradigm for Facial Expression Recognition](https://mlsanity.com/paper/2209.15402/) (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)
- [Partial-Label Regression](https://mlsanity.com/paper/2306.08968/) (2023)
- [Partial-Label Learning with a Reject Option](https://mlsanity.com/paper/2402.00592/) (2024)
- [Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2408.14369/) (2024)
- [Labels, Information, and Computation: Efficient Learning Using Sufficient Labels](https://mlsanity.com/paper/2104.09015/) (2021)
- [A two-steps approach to improve the performance of Android malware detectors](https://mlsanity.com/paper/2205.08265/) (2022)
- [Controller-Guided Partial Label Consistency Regularization with Unlabeled Data](https://mlsanity.com/paper/2210.11194/) (2022)
- [Towards Unbiased Exploration in Partial Label Learning](https://mlsanity.com/paper/2307.00465/) (2023)
- [Robust Representation Learning for Unreliable Partial Label Learning](https://mlsanity.com/paper/2308.16718/) (2023)
- [Partial-Label Learning with Conformal Candidate Cleaning](https://mlsanity.com/paper/2502.07661/) (2025)
- [A Survey of Learning on Small Data: Generalization, Optimization, and Challenge](https://mlsanity.com/paper/2207.14443/) (2022)
- [ProPaLL: Probabilistic Partial Label Learning](https://mlsanity.com/paper/2208.09931/) (2022)
- [Do Orcas Have Semantic Language? Machine Learning to Predict Orca Behaviors Using Partially Labeled Vocalization Data](https://mlsanity.com/paper/2302.10983/) (2023)
- [Exploiting Counter-Examples for Active Learning with Partial labels](https://mlsanity.com/paper/2307.07413/) (2023)
- [Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning](https://mlsanity.com/paper/2312.11034/) (2023)
- [Robust Partial-Label Learning by Leveraging Class Activation Values](https://mlsanity.com/paper/2502.11743/) (2025)
- [CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels](https://mlsanity.com/paper/2605.23254/) (2026)

## Abstract (arXiv)

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.