  Noisy partial label learning (noisy PLL) is an important branch of weakly
supervised learning. Unlike PLL where the ground-truth label must conceal in
the candidate label set, noisy PLL relaxes this constraint and allows the
ground-truth label may not be in the candidate label set. To address this
challenging problem, most of the existing works attempt to detect noisy samples
and estimate the ground-truth label for each noisy sample. However, detection
errors are unavoidable. These errors can accumulate during training and
continuously affect model optimization. To this end, we propose a novel
framework for noisy PLL with theoretical guarantees, called ``Adjusting Label
Importance Mechanism (ALIM)''. It aims to reduce the negative impact of
detection errors by trading off the initial candidate set and model outputs.
ALIM is a plug-in strategy that can be integrated with existing PLL approaches.
Experimental results on benchmark datasets demonstrate that our method can
achieve state-of-the-art performance on noisy PLL.
\textcolor[rgb]{0.93,0.0,0.47}{Our code can be found in Supplementary
Material}.
