  Partial-label learning (PLL) is an important branch of weakly supervised
learning where the single ground truth resides in a set of candidate labels,
while the research rarely considers the label imbalance. A recent study for
imbalanced partial-Label learning proposed that the combinatorial challenge of
partial-label learning and long-tail learning lies in matching between a decent
marginal prior distribution with drawing the pseudo labels. However, we believe
that even if the pseudo label matches the prior distribution, the tail classes
will still be difficult to learn because the total weight is too small.
Therefore, we propose a pseudo-label regularization technique specially
designed for PLL. By punishing the pseudo labels of head classes, our method
implements state-of-art under the standardized benchmarks compared to the
previous PLL methods.
