  In this paper, we study the partial multi-label (PML) image classification
problem, where each image is annotated with a candidate label set consists of
multiple relevant labels and other noisy labels. Existing PML methods typically
design a disambiguation strategy to filter out noisy labels by utilizing prior
knowledge with extra assumptions, which unfortunately is unavailable in many
real tasks. Furthermore, because the objective function for disambiguation is
usually elaborately designed on the whole training set, it can be hardly
optimized in a deep model with SGD on mini-batches. In this paper, for the
first time we propose a deep model for PML to enhance the representation and
discrimination ability. On one hand, we propose a novel curriculum based
disambiguation strategy to progressively identify ground-truth labels by
incorporating the varied difficulties of different classes. On the other hand,
a consistency regularization is introduced for model retraining to balance
fitting identified easy labels and exploiting potential relevant labels.
Extensive experimental results on the commonly used benchmark datasets show the
proposed method significantly outperforms the SOTA methods.
