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 centers on NPLL and
presents a minimalistic framework called SARI 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 and
standard regularization techniques. The classifier's features and predictions
are subsequently employed to refine and enhance the accuracy of pseudo-labels.
SARI combines the strengths of Average Based Strategies (in pseudo labelling)
and Identification Based Strategies (in classifier training) from the
literature. We perform thorough experiments on seven datasets and compare SARI
against nine NPLL and PLL methods from the prior art. SARI achieves
state-of-the-art results in almost all studied settings, obtaining substantial
gains in fine-grained classification and extreme noise settings.

SARI is a minimalistic framework for noisy partial label learning that combines average-based and identification-based strategies, achieving state-of-the-art results in fine-grained classification and extreme noise settings.