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 centers on NPLL and presents a minimalistic framework called SARI 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 and standard regularization techniques. The classifier's features and predictions are subsequently employed to refine and enhance the accuracy of pseudo-labels. SARI combines the strengths of Average Based Strategies (in pseudo labelling) and Identification Based Strategies (in classifier training) from the literature. We perform thorough experiments on seven datasets and compare SARI against nine NPLL and PLL methods from the prior art. SARI achieves state-of-the-art results in almost all studied settings, obtaining substantial gains in fine-grained classification and extreme noise settings.

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）相关联，且其中仅有一个为真实标签。噪声部分标签学习（NPLL）通过允许部分标签可能不包含真实标签来放宽这一约束，从而增强了问题的实用性。我们的工作聚焦于NPLL，并提出了一种极简框架SARI，该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，随后将这些伪标签-图像对用于训练结合标签平滑与标准正则化技术的深度神经网络分类器。分类器的特征与预测结果进一步用于优化和提升伪标签的准确性。SARI融合了文献中基于平均策略（伪标签生成）与基于识别策略（分类器训练）的优势。我们在七个数据集上进行了全面实验，并将SARI与九种现有NPLL及PLL方法进行对比。SARI在几乎所有研究场景中均取得了最先进的结果，在细粒度分类与极端噪声设置下获得了显著性能提升。