  Partial label learning is a type of weakly supervised learning, where each
training instance corresponds to a set of candidate labels, among which only
one is true. In this paper, we introduce ProPaLL, a novel probabilistic
approach to this problem, which has at least three advantages compared to the
existing approaches: it simplifies the training process, improves performance,
and can be applied to any deep architecture. Experiments conducted on
artificial and real-world datasets indicate that ProPaLL outperforms the
existing approaches.
