  Partial Label Learning (PLL) is a type of weakly supervised learning where
each training instance is assigned a set of candidate labels, but only one
label is the ground-truth. However, this idealistic assumption may not always
hold due to potential annotation inaccuracies, meaning the ground-truth may not
be present in the candidate label set. This is known as Unreliable Partial
Label Learning (UPLL) that introduces an additional complexity due to the
inherent unreliability and ambiguity of partial labels, often resulting in a
sub-optimal performance with existing methods. To address this challenge, we
propose the Unreliability-Robust Representation Learning framework (URRL) that
leverages unreliability-robust contrastive learning to help the model fortify
against unreliable partial labels effectively. Concurrently, we propose a dual
strategy that combines KNN-based candidate label set correction and
consistency-regularization-based label disambiguation to refine label quality
and enhance the ability of representation learning within the URRL framework.
Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art PLL methods on various datasets with diverse degrees of
unreliability and ambiguity. Furthermore, we provide a theoretical analysis of
our approach from the perspective of the expectation maximization (EM)
algorithm. Upon acceptance, we pledge to make the code publicly accessible.
