  Partial Label (PL) learning refers to the task of learning from the partially
labeled data, where each training instance is ambiguously equipped with a set
of candidate labels but only one is valid. Advances in the recent deep PL
learning literature have shown that the deep learning paradigms, e.g.,
self-training, contrastive learning, or class activate values, can achieve
promising performance. Inspired by the impressive success of deep
Semi-Supervised (SS) learning, we transform the PL learning problem into the SS
learning problem, and propose a novel PL learning method, namely Partial Label
learning with Semi-supervised Perspective (PLSP). Specifically, we first form
the pseudo-labeled dataset by selecting a small number of reliable
pseudo-labeled instances with high-confidence prediction scores and treating
the remaining instances as pseudo-unlabeled ones. Then we design a SS learning
objective, consisting of a supervised loss for pseudo-labeled instances and a
semantic consistency regularization for pseudo-unlabeled instances. We further
introduce a complementary regularization for those non-candidate labels to
constrain the model predictions on them to be as small as possible. Empirical
results demonstrate that PLSP significantly outperforms the existing PL
baseline methods, especially on high ambiguity levels. Code available:
https://github.com/changchunli/PLSP.
