  Partial Label Learning (PLL) aims to train a classifier when each training
instance is associated with a set of candidate labels, among which only one is
correct but is not accessible during the training phase. The common strategy
dealing with such ambiguous labeling information is to disambiguate the
candidate label sets. Nonetheless, existing methods ignore the disambiguation
difficulty of instances and adopt the single-trend training mechanism. The
former would lead to the vulnerability of models to the false positive labels
and the latter may arouse error accumulation problem. To remedy these two
drawbacks, this paper proposes a novel approach termed "Network Cooperation
with Progressive Disambiguation" (NCPD) for PLL. Specifically, we devise a
progressive disambiguation strategy of which the disambiguation operations are
performed on simple instances firstly and then gradually on more complicated
ones. Therefore, the negative impacts brought by the false positive labels of
complicated instances can be effectively mitigated as the disambiguation
ability of the model has been strengthened via learning from the simple
instances. Moreover, by employing artificial neural networks as the backbone,
we utilize a network cooperation mechanism which trains two networks
collaboratively by letting them interact with each other. As two networks have
different disambiguation ability, such interaction is beneficial for both
networks to reduce their respective disambiguation errors, and thus is much
better than the existing algorithms with single-trend training process.
Extensive experimental results on various benchmark and practical datasets
demonstrate the superiority of our NCPD to other state-of-the-art PLL methods.
