  Noisy labels can impair model performance, making the study of learning with
noisy labels an important topic. Two conventional approaches are noise modeling
and noise detection. However, these two methods are typically studied
independently, and there has been limited work on their collaboration. In this
work, we explore the integration of these two approaches, proposing an
interconnected structure with three crucial blocks: noise modeling, source
knowledge identification, and enhanced noise detection using noise
source-knowledge-integration methods. This collaboration structure offers
advantages such as discriminating hard negatives and preserving genuinely clean
labels that might be suspiciously noisy. Our experiments on four datasets,
featuring three types of noise and different combinations of each block,
demonstrate the efficacy of these components' collaboration. Our collaborative
structure methods achieve up to a 10% increase in top-1 classification accuracy
in synthesized noise datasets and 3-5% in real-world noisy datasets. The
results also suggest that these components make distinct contributions to
overall performance across various noise scenarios. These findings provide
valuable insights for designing noisy label learning methods customized for
specific noise scenarios in the future. Our code is accessible to the public.
