  In noisy label learning, estimating noisy class posteriors plays a
fundamental role for developing consistent classifiers, as it forms the basis
for estimating clean class posteriors and the transition matrix. Existing
methods typically learn noisy class posteriors by training a classification
model with noisy labels. However, when labels are incorrect, these models may
be misled to overemphasize the feature parts that do not reflect the instance
characteristics, resulting in significant errors in estimating noisy class
posteriors. To address this issue, this paper proposes to augment the
supervised information with part-level labels, encouraging the model to focus
on and integrate richer information from various parts. Specifically, our
method first partitions features into distinct parts by cropping instances,
yielding part-level labels associated with these various parts. Subsequently,
we introduce a novel single-to-multiple transition matrix to model the
relationship between the noisy and part-level labels, which incorporates
part-level labels into a classifier-consistent framework. Utilizing this
framework with part-level labels, we can learn the noisy class posteriors more
precisely by guiding the model to integrate information from various parts,
ultimately improving the classification performance. Our method is
theoretically sound, while experiments show that it is empirically effective in
synthetic and real-world noisy benchmarks.
