  Partial-label learning (PLL) generally focuses on inducing a noise-tolerant
multi-class classifier by training on overly-annotated samples, each of which
is annotated with a set of labels, but only one is the valid label. A basic
promise of existing PLL solutions is that there are sufficient partial-label
(PL) samples for training. However, it is more common than not to have just few
PL samples at hand when dealing with new tasks. Furthermore, existing few-shot
learning algorithms assume precise labels of the support set; as such,
irrelevant labels may seriously mislead the meta-learner and thus lead to a
compromised performance. How to enable PLL under a few-shot learning setting is
an important problem, but not yet well studied. In this paper, we introduce an
approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance
metric learning by an embedding network and rectifying prototypes on the tasks
previously encountered. Next, it calculates the prototype of each class of a
new task in the embedding network. An unseen example can then be classified via
its distance to each prototype. Experimental results on widely-used few-shot
datasets (Omniglot and miniImageNet) demonstrate that our FsPLL can achieve a
superior performance than the state-of-the-art methods across different
settings, and it needs fewer samples for quickly adapting to new tasks.
