  Under partial-label learning (PLL) where, for each training instance, only a
set of ambiguous candidate labels containing the unknown true label is
accessible, contrastive learning has recently boosted the performance of PLL on
vision tasks, attributed to representations learned by contrasting the
same/different classes of entities. Without access to true labels, positive
points are predicted using pseudo-labels that are inherently noisy, and
negative points often require large batches or momentum encoders, resulting in
unreliable similarity information and a high computational overhead. In this
paper, we rethink a state-of-the-art contrastive PLL method PiCO[24], inspiring
the design of a simple framework termed PaPi (Partial-label learning with a
guided Prototypical classifier), which demonstrates significant scope for
improvement in representation learning, thus contributing to label
disambiguation. PaPi guides the optimization of a prototypical classifier by a
linear classifier with which they share the same feature encoder, thus
explicitly encouraging the representation to reflect visual similarity between
categories. It is also technically appealing, as PaPi requires only a few
components in PiCO with the opposite direction of guidance, and directly
eliminates the contrastive learning module that would introduce noise and
consume computational resources. We empirically demonstrate that PaPi
significantly outperforms other PLL methods on various image classification
tasks.
