SHIP: Structural Hierarchies for Instance-Dependent Partial Labels

Abstract

Partial label learning (PLL) aims to train classification models under conditions where each training sample is associated with a candidate set of labels. This set contains multiple labels among which only one is correct. This work addresses instance-dependent noise in PLL by leveraging hierarchical structures within the label space. We introduce a method to derive label hierarchies from instance-dependent partial labels. Subsequently we propose Structural Hierarchies for Instance-dependent Partial label (SHIP). SHIP is a modular component that integrates into deep learning architectures with applications that have intrinsic hierarchies. SHIP harnesses label hierarchy to enhance instance-dependent PLL performance across various deep-learning algorithms with hierarchy in the dataset. We conduct experiments on five publicly available benchmark datasets with four recent PLL algorithms. Experimental results show that incorporating SHIP into state-of-the-art architectures yields up to a 2.6% improvement in accuracy when hierarchies are present in the data. Moreover when the number of classes is high SHIP achieves up to a 2.5% reduction in mean mistake severity highlighting its effectiveness in mitigating error severity.