I. Introduction
Partial label learning (PLL) [1]–[3], in contrast to semisupervised learning (where some instances have definite labels while others have no labels), is a weakly supervised learning problem [4], [5]. In PLL, each instance has a collection of candidate labels, with only one ground-truth label and the rest being false-positive labels, resulting in ambiguity while training classification models.