# Progressive Purification for Instance-Dependent Partial Label Learning

arXiv: https://arxiv.org/abs/2206.00830 · HTML: https://mlsanity.com/paper/2206.00830/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Learning with Feature-Dependent Label Noise: A Progressive Approach](https://mlsanity.com/paper/2103.07756/) (2021)
- [Learning with Biased Complementary Labels](https://mlsanity.com/paper/1711.09535/) (2017)
- [Provably Consistent Partial-Label Learning](https://mlsanity.com/paper/2007.08929/) (2020)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [Learning with Multiple Complementary Labels](https://mlsanity.com/paper/1912.12927/) (2019)
- [Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2110.12911/) (2021)
- [Partial Label Learning with Self-Guided Retraining](https://mlsanity.com/paper/1902.03045/) (2019)
- [Part-dependent Label Noise: Towards Instance-dependent Label Noise](https://mlsanity.com/paper/2006.07836/) (2020)
- [One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement](https://mlsanity.com/paper/2206.00517/) (2022)
- [On the Robustness of Average Losses for Partial-Label Learning](https://mlsanity.com/paper/2106.06152/) (2021)
- [Network Cooperation with Progressive Disambiguation for Partial Label Learning](https://mlsanity.com/paper/2002.11919/) (2020)
- [Learning with Proper Partial Labels](https://mlsanity.com/paper/2112.12303/) (2021)

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

- [Realistic Evaluation of Deep Partial-Label Learning Algorithms](https://mlsanity.com/paper/2502.10184/) (2025)
- [CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning](https://mlsanity.com/paper/2303.10365/) (2023)
- [Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical](https://mlsanity.com/paper/2311.15502/) (2023)
- [Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations](https://mlsanity.com/paper/2305.12715/) (2023)
- [Multi-Instance Partial-Label Learning with Margin Adjustment](https://mlsanity.com/paper/2501.12597/) (2025)
- [IRNet: Iterative Refinement Network for Noisy Partial Label Learning](https://mlsanity.com/paper/2211.04774/) (2022)
- [Partial-Label Learning with a Reject Option](https://mlsanity.com/paper/2402.00592/) (2024)
- [Partial-Label Learning with Conformal Candidate Cleaning](https://mlsanity.com/paper/2502.07661/) (2025)
- [Tuning the Right Foundation Models is What you Need for Partial Label Learning](https://mlsanity.com/paper/2506.05027/) (2025)
- [A2LC: Active and Automated Label Correction for Semantic Segmentation](https://mlsanity.com/paper/2506.11599/) (2025)
- [Can Class-Priors Help Single-Positive Multi-Label Learning?](https://mlsanity.com/paper/2309.13886/) (2023)
- [Progressively Label Enhancement for Large Language Model Alignment](https://mlsanity.com/paper/2408.02599/) (2024)
- [Robust Partial-Label Learning by Leveraging Class Activation Values](https://mlsanity.com/paper/2502.11743/) (2025)
- [Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2512.17788/) (2025)
- [Learning from Complementary Features](https://mlsanity.com/paper/2408.14788/) (2024)
- [Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning](https://mlsanity.com/paper/2410.20797/) (2024)
- [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)
- [Learning Robust Diffusion Models from Imprecise Supervision](https://mlsanity.com/paper/2510.03016/) (2025)
- [Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation](https://mlsanity.com/paper/2510.21300/) (2025)
- [Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2603.04825/) (2026)
- [Learnability with Partial Labels and Adaptive Nearest Neighbors](https://mlsanity.com/paper/2603.15781/) (2026)
- [Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions](https://mlsanity.com/paper/2608.06896/) (2026)

## Abstract (arXiv)

Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. In the last few years, the instance-independent generation process of candidate labels has been extensively studied, on the basis of which many theoretical advances have been made in PLL. Nevertheless, the candidate labels are always instance-dependent in practice and there is no theoretical guarantee that the model trained on the instance-dependent PLL examples can converge to an ideal one. In this paper, a theoretically grounded and practically effective approach named POP, i.e. PrOgressive Purification for instance-dependent partial label learning, is proposed. Specifically, POP updates the learning model and purifies each candidate label set progressively in every epoch. Theoretically, we prove that POP enlarges the region appropriately fast where the model is reliable, and eventually approximates the Bayes optimal classifier with mild assumptions. Technically, POP is flexible with arbitrary PLL losses and could improve the performance of the previous PLL losses in the instance-dependent case. Experiments on the benchmark datasets and the real-world datasets validate the effectiveness of the proposed method.