  Partial-label learning (PLL) is a peculiar weakly-supervised learning task
where the training samples are generally associated with a set of candidate
labels instead of single ground truth. While a variety of label disambiguation
methods have been proposed in this domain, they normally assume a
class-balanced scenario that may not hold in many real-world applications.
Empirically, we observe degenerated performance of the prior methods when
facing the combinatorial challenge from the long-tailed distribution and
partial-labeling. In this work, we first identify the major reasons that the
prior work failed. We subsequently propose SoLar, a novel Optimal
Transport-based framework that allows to refine the disambiguated labels
towards matching the marginal class prior distribution. SoLar additionally
incorporates a new and systematic mechanism for estimating the long-tailed
class prior distribution under the PLL setup. Through extensive experiments,
SoLar exhibits substantially superior results on standardized benchmarks
compared to the previous state-of-the-art PLL methods. Code and data are
available at: https://github.com/hbzju/SoLar .
