  Machine learning in the context of noise is a challenging but practical
setting to plenty of real-world applications. Most of the previous approaches
in this area focus on the pairwise relation (casual or correlational
relationship) with noise, such as learning with noisy labels. However, the
group noise, which is parasitic on the coarse-grained accurate relation with
the fine-grained uncertainty, is also universal and has not been well
investigated. The challenge under this setting is how to discover true pairwise
connections concealed by the group relation with its fine-grained noise. To
overcome this issue, we propose a novel Max-Matching method for learning with
group noise. Specifically, it utilizes a matching mechanism to evaluate the
relation confidence of each object w.r.t. the target, meanwhile considering the
Non-IID characteristics among objects in the group. Only the most confident
object is considered to learn the model, so that the fine-grained noise is
mostly dropped. The performance on arange of real-world datasets in the area of
several learning paradigms demonstrates the effectiveness of Max-Matching
