# PLOOD: Partial Label Learning with Out-of-distribution Objects

arXiv: https://arxiv.org/abs/2403.06681 · HTML: https://mlsanity.com/paper/2403.06681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation](https://mlsanity.com/paper/2012.06575/) (2020)
- [Can multi-label classification networks know what they don't know?](https://mlsanity.com/paper/2109.14162/) (2021)
- [Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources](https://mlsanity.com/paper/2311.03236/) (2023)
- [On the Robustness of Average Losses for Partial-Label Learning](https://mlsanity.com/paper/2106.06152/) (2021)
- [Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels](https://mlsanity.com/paper/2304.10539/) (2023)
- [Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning](https://mlsanity.com/paper/2305.02795/) (2023)
- [Out-of-distribution Detection with Boundary Aware Learning](https://mlsanity.com/paper/2112.11648/) (2021)
- [CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning](https://mlsanity.com/paper/2303.10365/) (2023)
- [Towards Effective Visual Representations for Partial-Label Learning](https://mlsanity.com/paper/2305.06080/) (2023)
- [YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection](https://mlsanity.com/paper/2212.02081/) (2022)
- [Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets](https://mlsanity.com/paper/2207.01573/) (2022)
- [Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images](https://mlsanity.com/paper/2206.15353/) (2022)
- [Unreliable Partial Label Learning with Recursive Separation](https://mlsanity.com/paper/2302.09891/) (2023)

## Abstract (arXiv)

Existing Partial Label Learning (PLL) methods posit that training and test data adhere to the same distribution, a premise that frequently does not hold in practical application where Out-of-Distribution (OOD) objects are present. We introduce the OODPLL paradigm to tackle this significant yet underexplored issue. And our newly proposed PLOOD framework enables PLL to tackle OOD objects through Positive-Negative Sample Augmented (PNSA) feature learning and Partial Energy (PE)-based label refinement. The PNSA module enhances feature discrimination and OOD recognition by simulating in- and out-of-distribution instances, which employ structured positive and negative sample augmentation, in contrast to conventional PLL methods struggling to distinguish OOD samples. The PE scoring mechanism combines label confidence with energy-based uncertainty estimation, thereby reducing the impact of imprecise supervision and effectively achieving label disambiguation. Experimental results on CIFAR-10 and CIFAR-100, alongside various OOD datasets, demonstrate that conventional PLL methods exhibit substantial degradation in OOD scenarios, underscoring the necessity of incorporating OOD considerations in PLL approaches. Ablation studies show that PNSA feature learning and PE-based label refinement are necessary for PLOOD to work, offering a robust solution for open-set PLL problems.