Partial label learning (PLL) is a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this constraint by allowing some partial labels to not contain the true label, enhancing the practicality of the problem. Our work centres on NPLL and presents a minimalistic framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm. These pseudo-label and image pairs are then used to train a deep neural network classifier with label smoothing. The classifier's features and predictions are subsequently employed to refine and enhance the accuracy of pseudo-labels. We perform thorough experiments on seven datasets and compare against nine NPLL and PLL methods. We achieve state-of-the-art results in all studied settings from the prior literature, obtaining substantial gains in fine-grained classification and extreme noise scenarios. Further, we show the promising generalisation capability of our framework in realistic crowd-sourced datasets.

翻译：部分标签学习（PLL）是一种弱监督学习范式，其中每个训练实例与一组候选标签（部分标签）配对，其中一个标签为真实标签。噪声部分标签学习（NPLL）放宽了这一约束，允许部分标签不包含真实标签，从而增强了该问题的实用性。我们的工作聚焦于NPLL，提出了一种极简框架：首先通过加权最近邻算法利用噪声部分标签为图像分配伪标签，然后使用这些伪标签与图像对训练一个采用标签平滑的深度神经网络分类器。随后，该分类器的特征和预测被用于优化伪标签并提升其准确性。我们在七个数据集上进行了全面实验，并与九种NPLL和PLL方法进行了比较。在所有现有文献研究的设定中，我们的方法均取得了最先进的结果，在细粒度分类和极端噪声场景下获得了显著提升。此外，我们展示了该框架在现实众包数据集中具有优异的泛化能力。