  Partial label learning (PLL) is a typical weakly supervised learning problem,
where each training example is associated with a set of candidate labels among
which only one is true. Most existing PLL approaches assume that the incorrect
labels in each training example are randomly picked as the candidate labels and
model the generation process of the candidate labels in a simple way. However,
these approaches usually do not perform as well as expected due to the fact
that the generation process of the candidate labels is always
instance-dependent. Therefore, it deserves to be modeled in a refined way. In
this paper, we consider instance-dependent PLL and assume that the generation
process of the candidate labels could decompose into two sequential parts,
where the correct label emerges first in the mind of the annotator but then the
incorrect labels related to the feature are also selected with the correct
label as candidate labels due to uncertainty of labeling. Motivated by this
consideration, we propose a novel PLL method that performs Maximum A Posterior
(MAP) based on an explicitly modeled generation process of candidate labels via
decomposed probability distribution models. Extensive experiments on manually
corrupted benchmark datasets and real-world datasets validate the effectiveness
of the proposed method. Source code is available at
https://github.com/palm-ml/idgp.
