  Partial label learning (PLL) is a weakly-supervised learning paradigm where
each training instance is paired with a set of candidate labels (partial
label), one of which is the true label. Noisy PLL (NPLL) relaxes this
constraint by allowing some partial labels to not contain the true label,
enhancing the practicality of the problem. Our work centres on NPLL and
presents a minimalistic framework that initially assigns pseudo-labels to
images by exploiting the noisy partial labels through a weighted nearest
neighbour algorithm. These pseudo-label and image pairs are then used to train
a deep neural network classifier with label smoothing. The classifier's
features and predictions are subsequently employed to refine and enhance the
accuracy of pseudo-labels. We perform thorough experiments on seven datasets
and compare against nine NPLL and PLL methods. We achieve state-of-the-art
results in all studied settings from the prior literature, obtaining
substantial gains in fine-grained classification and extreme noise scenarios.
Further, we show the promising generalisation capability of our framework in
realistic crowd-sourced datasets.
