  Partial label (PL) learning tackles the problem where each training instance
is associated with a set of candidate labels that include both the true label
and irrelevant noise labels. In this paper, we propose a novel multi-level
generative model for partial label learning (MGPLL), which tackles the problem
by learning both a label level adversarial generator and a feature level
adversarial generator under a bi-directional mapping framework between the
label vectors and the data samples. Specifically, MGPLL uses a conditional
noise label generation network to model the non-random noise labels and perform
label denoising, and uses a multi-class predictor to map the training instances
to the denoised label vectors, while a conditional data feature generator is
used to form an inverse mapping from the denoised label vectors to data
samples. Both the noise label generator and the data feature generator are
learned in an adversarial manner to match the observed candidate labels and
data features respectively. Extensive experiments are conducted on synthesized
and real-world partial label datasets. The proposed approach demonstrates the
state-of-the-art performance for partial label learning.
