# Network Cooperation with Progressive Disambiguation for Partial Label Learning

arXiv: https://arxiv.org/abs/2002.11919 · HTML: https://mlsanity.com/paper/2002.11919/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [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)
- [On the Robustness of Average Losses for Partial-Label Learning](https://mlsanity.com/paper/2106.06152/) (2021)
- [Towards Effective Visual Representations for Partial-Label Learning](https://mlsanity.com/paper/2305.06080/) (2023)
- [Tuning Vision-Language Models with Candidate Labels by Prompt Alignment](https://mlsanity.com/paper/2407.07638/) (2024)
- [Unreliable Partial Label Learning with Recursive Separation](https://mlsanity.com/paper/2302.09891/) (2023)
- [Tuning the Right Foundation Models is What you Need for Partial Label Learning](https://mlsanity.com/paper/2506.05027/) (2025)
- [Decompositional Generation Process for Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2204.03845/) (2022)
- [Meta Objective Guided Disambiguation for Partial Label Learning](https://mlsanity.com/paper/2208.12459/) (2022)
- [AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning](https://mlsanity.com/paper/2407.15036/) (2024)
- [Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning](https://mlsanity.com/paper/2410.20797/) (2024)
- [Diffusion Disambiguation Models for Partial Label Learning](https://mlsanity.com/paper/2507.00411/) (2025)

## Abstract (arXiv)

Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase. The common strategy dealing with such ambiguous labeling information is to disambiguate the candidate label sets. Nonetheless, existing methods ignore the disambiguation difficulty of instances and adopt the single-trend training mechanism. The former would lead to the vulnerability of models to the false positive labels and the latter may arouse error accumulation problem. To remedy these two drawbacks, this paper proposes a novel approach termed "Network Cooperation with Progressive Disambiguation" (NCPD) for PLL. Specifically, we devise a progressive disambiguation strategy of which the disambiguation operations are performed on simple instances firstly and then gradually on more complicated ones. Therefore, the negative impacts brought by the false positive labels of complicated instances can be effectively mitigated as the disambiguation ability of the model has been strengthened via learning from the simple instances. Moreover, by employing artificial neural networks as the backbone, we utilize a network cooperation mechanism which trains two networks collaboratively by letting them interact with each other. As two networks have different disambiguation ability, such interaction is beneficial for both networks to reduce their respective disambiguation errors, and thus is much better than the existing algorithms with single-trend training process. Extensive experimental results on various benchmark and practical datasets demonstrate the superiority of our NCPD to other state-of-the-art PLL methods.