  Partial label learning (PLL) is an important problem that allows each
training example to be labeled with a coarse candidate set, which well suits
many real-world data annotation scenarios with label ambiguity. Despite the
promise, the performance of PLL often lags behind the supervised counterpart.
In this work, we bridge the gap by addressing two key research challenges in
PLL -- representation learning and label disambiguation -- in one coherent
framework. Specifically, our proposed framework PiCO consists of a contrastive
learning module along with a novel class prototype-based label disambiguation
algorithm. PiCO produces closely aligned representations for examples from the
same classes and facilitates label disambiguation. Theoretically, we show that
these two components are mutually beneficial, and can be rigorously justified
from an expectation-maximization (EM) algorithm perspective. Moreover, we study
a challenging yet practical noisy partial label learning setup, where the
ground-truth may not be included in the candidate set. To remedy this problem,
we present an extension PiCO+ that performs distance-based clean sample
selection and learns robust classifiers by a semi-supervised contrastive
learning algorithm. Extensive experiments demonstrate that our proposed methods
significantly outperform the current state-of-the-art approaches in standard
and noisy PLL tasks and even achieve comparable results to fully supervised
learning.
