  In partial multi-label learning (PML), each data example is equipped with a
candidate label set, which consists of multiple ground-truth labels and other
false-positive labels. Recently, graph-based methods, which demonstrate a good
ability to estimate accurate confidence scores from candidate labels, have been
prevalent to deal with PML problems. However, we observe that existing
graph-based PML methods typically adopt linear multi-label classifiers and thus
fail to achieve superior performance. In this work, we attempt to remove
several obstacles for extending them to deep models and propose a novel deep
Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we
introduce the instance-level and label-level similarities to recover label
confidences as well as exploit label dependencies. At each training epoch,
labels are propagated on the instance and label graphs to produce relatively
accurate pseudo-labels; then, we train the deep model to fit the numerical
labels. Moreover, we provide a careful analysis of the risk functions to
guarantee the robustness of the proposed model. Extensive experiments on
various synthetic datasets and three real-world PML datasets demonstrate that
PLAIN achieves significantly superior results to state-of-the-art methods.
